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IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalens Högskola. (Learning and Optimisation)ORCID iD: 0000-0002-3425-3837
2019 (English)Doctoral 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.

Place, publisher, year, edition, pages
Västerås: Mälardalen University , 2019. , p. 130
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 302
Keywords [en]
Differential Evolution; Adaptation; Memetic algorithm; Evolutionary Algorithm
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-45868ISBN: 978-91-7485-447-3 (print)OAI: oai:DiVA.org:mdh-45868DiVA, id: diva2:1366429
Public defence
2019-12-18, Delta, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 16317Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-10-29Bibliographically approved
List of papers
1. A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
Open this publication in new window or tab >>A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
2019 (English)In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 12, no 2, p. 795-808Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
ATLANTIS PRESS, 2019
Keywords
Differential evolution, L-SHADE, Memetic algorithm, Alopex, Local search, Optimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:mdh:diva-45267 (URN)10.2991/ijcis.d.190711.001 (DOI)000483992100029 ()2-s2.0-85073320189 (Scopus ID)
Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2019-10-31Bibliographically approved
2. Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation
Open this publication in new window or tab >>Enhancing Adaptive Differential Evolution Algorithms with Rank-Based Mutation Adaptation
2018 (English)In: 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2018, p. 103-109Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Evolutionary Algorithm, Differential Evolution, Mutation strategy, Adaptation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-41825 (URN)000451175500015 ()2-s2.0-85056286107 (Scopus ID)
Conference
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2019-10-29Bibliographically approved
3. Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
Open this publication in new window or tab >>Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters
2016 (English)In: Journal of Artificial Intelligence and Soft Computing Research, ISSN 2449-6499, Vol. 6, no 2, p. 103-118Article in journal (Refereed) Published
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.

Keywords
Differential evolution, Optimization, Parameter adaptation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-34771 (URN)10.1515/jaiscr-2016-0009 (DOI)000408865800004 ()2-s2.0-85009725987 (Scopus ID)
Available from: 2017-02-08 Created: 2017-02-02 Last updated: 2019-10-29Bibliographically approved
4. Designing optimal harmonic filters in power systems using greedy adaptive Differential Evolution
Open this publication in new window or tab >>Designing optimal harmonic filters in power systems using greedy adaptive Differential Evolution
2016 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2016Conference paper, Published 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.

Keywords
Bandpass filters, Evolutionary algorithms, Factory automation, Harmonic analysis, Harmonic distortion, Stochastic systems, Adaptive differential evolutions, Differential Evolution, Dynamic adjustment, Harmonic filtering, Optimization techniques, Population-based optimization, Power distribution system, Two control parameters, Optimization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-34044 (URN)10.1109/ETFA.2016.7733571 (DOI)000389524200078 ()2-s2.0-84996598844 (Scopus ID)978-1-5090-1314-2 (ISBN)
Conference
21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2016, 6 September 2016 through 9 September 2016
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2019-10-29Bibliographically approved
5. Differential evolution enhanced with eager random search for solving real-parameter optimization problems
Open this publication in new window or tab >>Differential evolution enhanced with eager random search for solving real-parameter optimization problems
2015 (English)In: International Journal of Advanced Research in Artificial Intelligence, 2015 IJARAI-15, ISSN 2165-4050, Vol. 4, no 12, p. 49-57Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Sweden: , 2015
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-32785 (URN)10.14569/IJARAI.2015.041208 (DOI)
Projects
ESS-H - Embedded Sensor Systems for Health Research ProfileEMOPAC - Evolutionary Multi-Objective Optimization and Its Applications in Analog Circuit Design
Available from: 2016-08-26 Created: 2016-08-24 Last updated: 2019-10-29Bibliographically approved

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