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  • 1.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    The Genetic Algorithm Census TransformManuscript (preprint) (Other academic)
  • 2.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Unbounded Sparse Census Transform using Genetic Algorithm2019In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), IEEE , 2019, p. 1616-1625Conference paper (Refereed)
    Abstract [en]

    The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

  • 3.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Enhancing Differential Evolution Algorithm for Solving Continuous Optimization Problems2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Differential Evolution (DE) has become one of the most important metaheuristics during the recent years, obtaining attractive results in solving many engineering optimization problems. However, the performance of DE is not always strong when seeking optimal solutions. It has two major problems in real world applications. First, it can easily get stuck in a local optimum or fail to generate better solutions before the population has converged. Secondly, its performance is significantly influenced by the control parameters, which are problem dependent and which vary in different regions of space under exploration.  It usually entails a time consuming trial-and-error procedure to set suitable parameters for DE in a specific problem, particularly for those practioners with limited knowledge and experience of using this technique.

     

    This thesis aims to develop new DE algorithms to address the two aforementioned problems. To mitigate the first problem, we studied the hybridization of DE with local search techniques to enhance the efficiency of search. The main idea is to apply a local search mechanism to the best individual in each generation of DE to exploit the most promising regions during the evolutionary processs so as to speed up the convergence or increase the chance to scape from local optima. Four local search strategies have been integrated  and tested in the global DE framework, leading to variants of the memetic DE algorithms with different properties concerning diversification and intensification. For tackling the second problem, we propose a greedy adaptation method for dynamic adjustment of the control parameters in DE. It is implemented by conducting greedy search repeatedly during the run of DE to reach better parameter assignments in the neighborhood of a current candidate. The candidates are assessed by considering both, the success rate and also fitness improvement of trial solutions against the target ones. The incorporation of this greedy parameter adaptation method into standard DE has led to a new adaptive DE algorithm, referred to as Greedy Adaptive Differential Evolution (GADE).

     

    The methods proposed in this thesis have been tested in different benchmark problems and compared with the state of the art algorithms, obtaining competitive results. Furthermore, the proposed GADE algorithm has been applied in an industrial scenario achieving more accurate results than those obtained by a standard DE algorithm. 

  • 4.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalens Högskola.
    IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Differential Evolution (DE) is a population-based algorithm that belongs to the Evolutionary algorithm family. During recent years, DE has become a popular algorithm in optimization due to its strength solving different types of optimization problems and due to its easy usage and implementation.

    However, how to choose proper mutation strategy and control parameters for DE presents a major difficulty in many real applications. Since both mutation strategy and DE control parameters are highly problem dependent, they have to be adapted to suite different search spaces and different problems. Failure in proper assignment for them will cause slow convergence in search or stagnation with a local optimum.

    Many researches have been conducted to tackle the above issues. The major efforts have been made in the following three directions. First, some works have been proposed that adapt the selection between various mutation strategies. But the choice of strategies in these methods has not considered the difference of quality of individuals in the population, which means that all individuals will acquire the same probability to select a mutation strategy from the candidates. This does not seem a very desired practice since solutions of large difference would require different mutation operators to reach improvement. Second, many works have been focusing on the adaptation of the control parameters of DE (mutation factor (F) and crossover rate (CR)). They mainly rely on previous successful F and CR values to update the probability functions that are used to generate new F and CR values. By doing this, they ignore the stochastic nature of the operators in DE such that weak F and CR values can also get success in producing better trial solutions. The use of such imprecise experiences of success would prevent the DE parameters from being adapted towards the most effective values in coming generations. Third, various local search methods have been incorporated into DE to enhance exploitation in promising regions so as to speed up the convergence to optima. It is important to properly adjust the characteristics of the local search in DE to achieve well balanced exploratory/exploitative behavior to solve complex optimization problems.

    This thesis aims to further improve the performance of DE by new adaptation and local search methods. The main results can be summarized in the following three aspects:

    1) Proposal of a new rank-based mutation adaptation method, which takes into account the quality of solutions in the population when adapting the selection probabilities of mutation strategies. This makes possible to treat solutions with distinct ranks (in quality) differently by using different selection probabilities for mutation operators.

    2) Development of improved parameter adaptation methods for DE, which emphasizes more reliable and fair evaluation of candidates (F and CR assignments) during the search process. It is suggested that greedy search being used as a fast and cheap technique to look for better parameter assignment for F and CR respectively in the neighborhood of a current candidate. Further, a joint parameter adaptation method is proposed that enables continuous update of the selection probabilities for F and CR pairs based on feedback acquired during the search.

    3) Proposal of new methods for better incorporation of local search into a DE algorithm. The Eager Random Search method is investigated as local search inside DE, which exhibits different exploratory-exploitative characteristics by using different probability density functions. More importantly, we propose a novel memetic framework in which Alopex local search (ALS) is performed in collaboration with a DE algorithm. The framework favors seamless connection between exploration and exploitation in the sense that the behavior of exploitation by ALS can be controlled by the status of global exploration by DE.

    The proposed methods and algorithms have been tested in a number of benchmark problems, obtaining competitive results compared with the state-of-the-art algorithms. Additionally, the Greedy Adaptive DE (GADE) algorithm (developed based on greedy search for DE parameters) has been tested in a real industrial problem, i.e., finding best component parameters to optimize the performance of harmonic filters for power transmission. GADE is shown to produce better harmonic filter systems with lower harmonic distortion than the standard DE.

  • 5.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ballesteros, Joaquin
    Mälardalen University, School of Innovation, Design and Engineering.
    Tidare, Jonatan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åstrand, Elaine
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm2019In: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 87-94Conference paper (Refereed)
    Abstract [en]

    Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

  • 6.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Evestedt, Magnus
    Industrial Systems, Prevas, Västerås, Sweden.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Application of adaptive differential evolution for model identification in furnace optimized control system2015In: IJCCI 2015 - Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015, p. 48-54Conference paper (Refereed)
    Abstract [en]

    Accurate system modelling is an important prerequisite for optimized process control in modern industrial scenarios. The task of parameter identification for a model can be considered as an optimization problem of searching for a set of continuous parameters to minimize the discrepancy between the model outputs and true output values. Differential Evolution (DE), as a class of population-based and global search algorithms, has strong potential to be employed here to solve this problem. Nevertheless, the performance of DE is rather sensitive to its two running parameters: scaling factor and crossover rate. Improper setting of these two parameters may cause weak performance of DE in real applications. This paper presents a new adaptive algorithm for DE, which does not require good parameter values to be specified by users in advance. Our new algorithm is established by integration of greedy search into the original DE algorithm. Greedy search is conducted repeatedly during the running of DE to reach better parameter assignments in the neighborhood. We have applied our adaptive DE algorithm for process model identification in a Furnace Optimized Control System (FOCS). The experiment results revealed that our adaptive DE algorithm yielded process models that estimated temperatures inside a furnace more precisely than those produced by using the original DE algorithm.

  • 7.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A new differential evolution algorithm with Alopex-based local search2016In: Lecture Notes in Computer Science, Volume 9692, 2016, p. 420-431Conference paper (Refereed)
    Abstract [en]

    Differential evolution (DE), as a class of biologically inspired and meta-heuristic techniques, has attained increasing popularity in solving many real world optimization problems. However, DE is not always successful. It can easily get stuck in a local optimum or an undesired stagnation condition. This paper proposes a new DE algorithm Differential Evolution with Alopex-Based Local Search (DEALS), for enhancing DE performance. Alopex uses local correlations between changes in individual parameters and changes in function values to estimate the gradient of the landscape. It also contains the idea of simulated annealing that uses temperature to control the probability of move directions during the search process. The results from experiments demonstrate that the use of Alopex as local search in DE brings substantial performance improvement over the standard DE algorithm. The proposed DEALS algorithm has also been shown to be strongly competitive (best rank) against several other DE variants with local search. 

  • 8.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters2016In: Journal of Artificial Intelligence and Soft Computing Research, ISSN 2449-6499, Vol. 6, no 2, p. 103-118Article in journal (Refereed)
    Abstract [en]

    Differential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

  • 9.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Alopex-based mutation strategy in Differential Evolution2017In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 1978-1984Conference paper (Refereed)
    Abstract [en]

    Differential Evolution represents a class of evolutionary algorithms that are highly competitive for solving numerical optimization problems. In a Differential Evolution algorithm, there are a few alternative mutation strategies, which may lead to good or a bad performance depending on the property of the problem. A new mutation strategy, called DE/Alopex/1, is proposed in this paper. This mutation strategy distinguishes itself from other mutation strategies in that it uses the fitness values of the individuals in the population in order to calculate the probabilities of move directions. The performance of DE/Alopex/1 has been evaluated on the benchmark suite from CEC2013. The results of the experiments show that DE/Alopex/1 outperforms some state-of-the-art mutation strategies. © 2017 IEEE.

  • 10.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Differential evolution enhanced with eager random search for solving real-parameter optimization problems2015In: International Journal of Advanced Research in Artificial Intelligence, 2015 IJARAI-15, ISSN 2165-4050, Vol. 4, no 12, p. 49-57Article in journal (Refereed)
    Abstract [en]

    Differential evolution (DE) presents a class of evolutionary computing techniques that appear effective to handle real parameter optimization tasks in many practical applications. However, the performance of DE is not always perfect to ensure fast convergence to the global optimum. It can easily get stagnation resulting in low precision of acquired results or even failure. This paper proposes a new memetic DE algorithm by incorporating Eager Random Search (ERS) to enhance the performance of a basic DE algorithm. ERS is a local search method that is eager to replace the current solution by a better candidate in the neighborhood. Three concrete local search strategies for ERS are further introduced and discussed, leading to variants of the proposed memetic DE algorithm. In addition, only a small subset of randomly selected variables is used in each step of the local search for randomly deciding the next trial solution. The results of tests on a set of benchmark problems have demonstrated that the hybridization of DE with Eager Random Search can substantially augment DE algorithms to find better or more precise solutions while not requiring extra computing resources.

  • 11.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Eager Random Search for Differential Evolution in Continuous Optimization2015In: PROGRESS IN ARTIFICIAL INTELLIGENCE, 2015, p. 286-291Conference paper (Refereed)
    Abstract [en]

    This paper proposes a memetic computing algorithm by incorporating Eager Random Search (ERS) into differential evolution (DE) to enhance its search ability. ERS is a local search method that is eager to move to a position that is identified as better than the current one without considering other opportunities. Forsaking optimality of moves in ERS is advantageous to increase the randomness and diversity of search for avoiding premature convergence. Three concrete local search strategies within ERS are introduced and discussed, leading to variants of the proposed memetic DE algorithm. The results of evaluations on a set of benchmark problems have demonstrated that the integration of DE with Eager Random Search can improve the performance of pure DE algorithms while not incurring extra computing expenses.

  • 12.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation2018In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 103-109Conference paper (Refereed)
    Abstract [en]

    Differential evolution has many mutation strategies which are problem dependent. Some Adaptive Differential Evolution techniques have been proposed tackling this problem. But therein all individuals are treated equally without taking into account how good these solutions are. In this paper, a new method called Ranked-based Mutation Adaptation (RAM) is proposed, which takes into consideration the ranking of an individual in the whole population. This method will assign different probabilities of choosing different mutation strategies to different groups in which the population is divided. RAM has been integrated into several well-known adaptive differential evolution algorithms and its performance has been tested on the benchmark suit proposed in CEC2014. The experimental results shows the use of RAM can produce generally better quality solutions than the original adaptive algorithms.

  • 13.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Greedy adaptation of control parameters in differential evolution for global optimization problems2015In: IEEE Conference on Evolutionary Computation 2015 ICEC15, 2015, p. 385-392Conference paper (Refereed)
    Abstract [en]

    Differential evolution (DE) is a very attractive evolutionary and meta-heuristic technique to solve many optimization problems in various real-world scenarios. However, the proper setting of control parameters of DE is highly dependent on the problem to solve as well as on the different stages of the search process. This paper proposes a new greedy adaptation method for dynamic adjustment of mutation factor and crossover rate in DE. The proposed method is based on the idea of greedy search to find better parameter assignment in the neighborhood of a current candidate. Our work emphasizes reliable evaluation of candidates via applying a candidate with a number of times in the search process. As our purpose is not merely to increase the success rate (the survival of more trial solutions) but also to accelerate the speed of fitness improvement, we suggest a new metric termed as progress rate to access the quality of candidates in support of the greedy search. This greedy parameter adaptation method has been incorporated into basic DE, leading to a new DE algorithm called Greedy Adaptive Differential Evolution (GADE). GADE was tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtains the best ranking among the counterparts in terms of the summation of relative errors across the benchmark functions.

  • 14.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Investigation of mutation strategies in differential evolution for solving global optimization problems2014In: Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I, Springer, 2014, no PART 1, p. 372-383Chapter in book (Refereed)
    Abstract [en]

    Differential evolution (DE) is one competitive form of evolutionary algorithms. It heavily relies on mutating solutions using scaled differences of randomly selected individuals from the population to create new solutions. The choice of a proper mutation strategy is important for the success of an DE algorithm. This paper presents an empirical investigation to examine and compare the different mutation strategies for global optimization problems. Both solution quality and computational expense of DE variants were evaluated with experiments conducted on a set of benchmark problems. The results of such comparative study would offer valuable insight and information to develop optimal or adaptive mutation strategies for future DE researches and applications. 

  • 15.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Investigation of Mutation Strategies in Differential Evolution for Solving Global Optimization Problems2014In: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING ICAISC 2014, PT I / [ed] Rutkowski, L Korytkowski, M Scherer, R Tadeusiewicz, R Zadeh, LA Zurada, JM, SPRINGER-VERLAG BERLIN , 2014, p. 372-383Conference paper (Refereed)
    Abstract [en]

    Differential evolution (DE) is one competitive form of evolutionary algorithms. It heavily relies on mutating solutions using scaled differences of randomly selected individuals from the population to create new solutions. The choice of a proper mutation strategy is important for the success of an DE algorithm. This paper presents an empirical investigation to examine and compare the different mutation strategies for global optimization problems. Both solution quality and computational expense of DE variants were evaluated with experiments conducted on a set of benchmark problems. The results of such comparative study would offer valuable insight and information to develop optimal or adaptive mutation strategies for future DE researches and applications.

  • 16.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Using Random Local Search Helps in Avoiding Local Optimum in Differential Evolution2014In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2014, Innsbruck, Austria, 2014, p. 413-420Conference paper (Refereed)
    Abstract [en]

    Differential Evolution is a stochastic and metaheuristic technique that has been proved powerful for solving real valued optimization problems in high dimensional spaces. However, Differential Evolution does not guarantee to con verge to the global optimum and it is easily to become trapped in a local optimum. In this paper, we aim to enhance Differential Evolution with Random Local Search to increase its ability to avoid local optimum. The proposed new algorithm is called Differential Evolution with Random Local Search (DERLS). The advantage of Random Local Search used in DERLS is that it is simple and fast in computation. The results of experiments have demonstrated that our DERLS algorithm can bring appreciable improvement for the acquired solutions in difficult optimization problems.

  • 17.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Molina, Daniel
    Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, Granada, Spain..
    Herrera, Francisco
    Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, Granada, Spain..
    A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search2019In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 12, no 2, p. 795-808Article in journal (Refereed)
    Abstract [en]

    Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/ regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been popular in recent years to enhance the exploitation of global algorithms via incorporation of local search. This paper proposes a new memetic framework to enhance DE algorithms using Alopex Local Search (MFDEALS). The novelty of the proposed MFDEALS framework lies in that the behavior of exploitation (by Alopex local search) can be controlled based on the DE global exploration status (population diversity and search stage). Additionally, an adaptive parameter inside the Alopex local search enables smooth transition of its behavior from exploratory to exploitative during the search process. A study of the important components of MFDEALS shows that there is a synergy between them. MFDEALS has been integrated with both the canonical DE method and the adaptive DE algorithm L-SHADE, leading to the MDEALS and ML-SHADEALS algorithms, respectively. Both algorithms were tested on the benchmark functions from the IEEE CEC'2014 Conference. The experiment results show that Memetic Differential Evolution with Alopex Local Search (MDEALS) not only improves the original DE algorithm but also outperforms other memetic DE algorithms by obtaining better quality solutions. Further, the comparison between ML-SHADEALS and L-SHADE demonstrates that applying the MFDEALS framework with Alopex local search can significantly enhance the performance of L-SHADE. 

  • 18.
    Leon, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Zenlander, Y.
    Research and Development, ABB FACTS, Västerås, Sweden.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Herrera, F.
    Dept. of Computer Science, Granada University, Granada, Spain.
    Designing optimal harmonic filters in power systems using greedy adaptive Differential Evolution2016In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2016Conference paper (Refereed)
    Abstract [en]

    Harmonic filtering has been widely applied to reduce harmonic distortion in power distribution systems. This paper investigates a new method of exploiting Differential Evolution (DE) to support the optimal design of harmonic filters. DE is a class of stochastic and population-based optimization algorithms that are expected to have stronger global ability than trajectory-based optimization techniques in locating the best component sizes for filters. However, the performance of DE is largely affected by its two control parameters: scaling factor and crossover rate, which are problem dependent. How to decide appropriate setting for these two parameters presents a practical difficulty in real applications. Greedy Adaptive Differential Evolution (GADE) algorithm is suggested in the paper as a more convenient and effective means to automatically optimize filter designs. GADE is attractive in that it does not require proper setting of the scaling factor and crossover rate prior to the running of the program. Instead it enables dynamic adjustment of the DE parameters during the course of search for performance improvement. The results of tests on several problem examples have demonstrated that the use of GADE leads to the discovery of better filter circuits facilitating less harmonic distortion than the basic DE method.

  • 19.
    Leon Ortiz, Miguel
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Evestedt, Magnus
    Industrial Systems, Prevas, Västerås, Sweden.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adaptive Differential Evolution Supports Automatic Model Calibration in Furnace Optimized Control System2017In: Computational Intelligence / [ed] Agostinho Rosa, Juan Julian Merelo, António Dourado, José M. Cadenas, Kurosh Madani, António Ruano and Joaquim Filipe, Germany: Springer, 2017, Vol. 669, p. 42-55Chapter in book (Other academic)
    Abstract [en]

    Model calibration represents the task of estimating the parameters of a process model to obtain a good match between observed and simulated behaviours. This can be considered as an optimization problem to search for model parameters that minimize the discrepancy between the model outputs and the corresponding features from the historical empirical data. This chapter investigates the use of Differential Evolution (DE), a competitive class of evolutionary algorithms, to solve calibration problems for nonlinear process models. The merits of DE include simple and compact structure, easy implementation, as well as high convergence speed. However, the good performance of DE relies on proper setting of its running parameters such as scaling factor and crossover probability, which are problem dependent and which can even vary in the different stages of the search. To mitigate this issue, we propose a new adaptive DE algorithm that dynamically adjusts its running parameters during its execution. The core of this new algorithm is the incorporated greedy local search, which is conducted in successive learning periods to continuously locate better parameter assignments in the optimization process. In case studies, we have applied our proposed adaptive DE algorithm for model calibration in a Furnace Optimized Control System. The statistical analysis of experimental results demonstrate that the proposed DE algorithm can support the creation of process models that are more accurate than those produced by standard DE.

  • 20.
    Ramos, Javier
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    MPADE: An Improved Adaptive Multi-Population Differential Evolution Algorithm Based on JADE2018In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 1139-1146Conference paper (Refereed)
    Abstract [en]

    JADE is an state-of-the-art adaptive differential evolution algorithm which implements "DE/current-to-pbest" as its mutation strategy, adapts its mutation factor and crossover rate, and uses an optional external archive to keep track of potential removed individuals in previous generations. This paper proposes MPADE, which extends JADE by using a multi-populated approach to solve high dimensional real-parameter optimization problems. This mechanism helps preventing the two well-known problems affecting the differential evolution algorithm performance, which are premature convergence and stagnation. The algorithm was tested using the benchmark functions in IEEE Congress on Evolutionary Computation 2014 test suite. MPADE was compared using Wilcoxon test to JADE algorithm and with other state-of-the-art algorithms that either use a multi-population approach or adapt their parameters. The experimental results show that the proposed new algorithm improves significantly its precursor and it is also suggested that other state-of-the-art algorithms could benefit from the multi-populated based approach.

  • 21.
    Suero, M.
    et al.
    Mälardalen University.
    Gassen, C. P.
    Mälardalen University.
    Mitic, D.
    Mälardalen University.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Deep Neural Network Model for Music Genre Recognition2020In: Advances in Intelligent Systems and Computing, vol. 1074, Springer , 2020, p. 377-384Conference paper (Refereed)
    Abstract [en]

    Convolutional neural networks (CNNs) have become increasingly important to deal with many image processing and pattern recognition problems. In order to use CNNs in music genre recognition, spectrograms (visual representation of the spectrum of frequencies of a signal as it varies with time) are usually employed as inputs of the network. Yet some other approaches used music features for genre classification as well. In this paper we propose a new deep network model combining CNN with a simple multi-layer neural network for music genre classification. Since other features are taken into account in the multi-layer network, the combined deep neural network has shown better accuracy than each of the single models in the experiments (Code available at: https://github.com/risengnom/Music-Genre-Recognition.).

  • 22.
    Tidare, Jonatan
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åstrand, Elaine
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Discriminating EEG spectral power related to mental imagery of closing and opening of hand2019In: 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), IEEE , 2019, p. 307-310Conference paper (Refereed)
    Abstract [en]

    ElectroEncephaloGram (EEG) spectral power has been extensively used to classify Mental Imagery (MI) of movements involving different body parts. However, there is an increasing need to enable classification of MI of movements within the same limb. In this work, EEG spectral power was recorded in seven subjects while they performed MI of closing (grip) and opening (extension of fingers) the hand. The EEG data was analyzed and the feasibility of classifying MI of the two movements were investigated using two different classification algorithms, a linear regression and a Convolutional Neural Network (CNN). Results show that only the CNN is able to significantly classify MI of opening and closing of the hand with an average classification accuracy of 60.4%. This indicates the presence of higher-order non-linear discriminatory information and demonstrates the potential of using CNN in classifying MI of same-limb movements.

  • 23.
    Xiong, Ning
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Differential evolution based on decomposition for solving multi-objective optimization problems2016In: ICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence, 2016, p. 512-516Conference paper (Refereed)
    Abstract [en]

    Optimization problems with multiple objectives are often encountered in many scientific and engineering scenarios. The prior works on multi-objective differential evolution (DE) have mainly focused on nondominated sorting of solutions to handle different objectives at the same time. This paper suggests a new approach to differential evolution which is based on decomposition of the original problem into a set of scalar optimization subproblems. We design a decomposition-based DE algorithm to optimize these scalar subproblems simultaneously by evolving a population of solutions with proper mutation and selection operators. Since the proposed DE algorithm does not involve pairwise comparison and non-dominated sorting of solutions, it would incur lower computational complexity than the dominance-based DE algorithms.

  • 24.
    Xiong, Ning
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Molina, Daniel
    Univ Cadiz, Spain.
    Ortiz, Miguel Leon
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Herrera, Francisco
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Univ Granada, Spain.
    A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends2015In: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, ISSN 1875-6891, Vol. 8, no 4, p. 606-636Article in journal (Refereed)
    Abstract [en]

    Metaheuristics has attained increasing interest for solving complex real-world problems. This paper studies the principles and the state-of-the-art of metaheuristic methods for engineering optimization. Both the classic and emerging approaches to optimization using metaheuristics are reviewed and analyzed. All the methods are discussed in three basic types: trajectory-based, in which in each step a new solution is created from the previous one; multi-trajectory-based, in which a multi-start mechanism is used; and population-based, where multiple new solutions are created considering a population of approximate solutions. We further discuss algorithms and strategies to handle multi-modal and multi-objective optimization tasks as well as methods for parallel implementation of metaheuristic algorithms. Then, different software frameworks for metaheuristics are introduced. Finally, several interesting directions are pointed out as future research trends.

  • 25.
    Xiong, Ning
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ortiz, Miguel Leon
    Mälardalen University, School of Innovation, Design and Engineering.
    Principles and state-of-the-art of engineering optimization techniques2013In: Advanced Engineering Computing and Applications in Sciences, Porto, Portugal, 2013, p. 36-42Conference paper (Refereed)
    Abstract [en]

    This paper gives a survey of the principles and the state-of-the-art of engineering optimization techniques. Both the classic and emerging approaches to nonlinear optimization problems are reviewed and analyzed. All the techniques are discussed in two basic types: point-based transition and population-based transition, depending on whether a single point or multiple points are generated as new approximate solution(s) in each step. We also consider multi-objective tasks as new application trend and point out the strong potential of population-based methods to tackle multiple objectives simultaneously.

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