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Differential evolution enhanced with eager random search for solving real-parameter optimization problems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).ORCID iD: 0000-0001-9857-4317
2015 (English)In: International Journal of Advanced Research in Artificial Intelligence, 2015 IJARAI-15, ISSN 2165-4050, Vol. 4, no 12, 49-57 p.Article 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. Vol. 4, no 12, 49-57 p.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-32785DOI: 10.14569/IJARAI.2015.041208OAI: oai:DiVA.org:mdh-32785DiVA: diva2:955711
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: 2016-10-26Bibliographically approved
In thesis
1. Enhancing Differential Evolution Algorithm for Solving Continuous Optimization Problems
Open this publication in new window or tab >>Enhancing Differential Evolution Algorithm for Solving Continuous Optimization Problems
2016 (English)Licentiate 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. 

Abstract [sv]

Differential Evolution (DE) har blivit en av de viktigaste metaheuristikerna under de senaste åren och har uppnått attraktiva resultat för att lösa många optimeringsproblem inom teknik. Dock är prestationen hos DE inte alltid framgångsrik när man söker optimala lösningar. Det finns två huvudsakliga problem för applikationer i den verkliga världen. Det första är att den lätt kan fastna i lokala optimum eller misslyckas att generera bättre lösningar före det att populationen (en grupp av lösningar) har hunnit konvergera. Det andra är att prestandan påverkas märkvärdigt av kontrollparametrar, vilkas optimala värden beror på problem som ska lösas och varierar mellan regioner i sökrymden. Detta innebär oftast ett tidskrävande trial-and-error förfarande för att hitta lämpliga parametrar till ett specifikt DE-problem, framför allt för utövare med begränsad kunskap och erfarenhet av DE.

 

Syftet med denna licentiatavhandling är att utveckla nya DE-algoritmer för att behandla de ovannämnda problemen. För att möta det första problemet så studerades hybridisering av DE och lokala söktekniker för att effektivisera sökningen. Tanken är att använda en lokal sökmekanism på den bästa individen i varje generation i DE-algoritmen och utnyttja de mest lovande regionerna under evolutionsprocessen för att snabba på konvergensen eller öka chansen att undvika lokala optimum. Fyra lokala sökstrategier har integrerats och testats i det globala DE-ramverket vilket har lett till fyra varianter av DE-algoritmerna med olika egenskaper beträffande diversifiering och intensifiering. Till det andra problemet föreslås en greedy adaptation method för dynamisk justering av kontrollparametrarna i DE. Den implementeras genom att utföra greedy search upprepade gånger under körningen av DE för att hitta bättre värden till kontrollparametrarna. Utvärderingen av parameterval baseras på både success rate och fitness improvement av trial lösningar jämfört med target lösningar. Sammanslagningen av DE och denna greedy parameter adaptation har lett till en ny adaptiv DE-algoritm som kallas Greedy Adaptive Differential Evolution (GADE).

 

Den föreslagna metoden i denna licentiatavhandling har testats i olika prestandamätningar och jämförts med state-of-the-art-algoritmer, med goda resultat. Dessutom har den föreslagna GADE-algoritmen använts i ett industriellt scenario och uppnådde då mer exakta resultat än den med en standard DE-algoritm.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2016
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 246
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-33466 (URN)978-91-7485-299-8 (ISBN)
Presentation
2016-12-15, Delta, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2016-10-26 Created: 2016-10-25 Last updated: 2016-11-25Bibliographically approved

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