A comparative study of local search within a surrogate-assisted multi-objective memetic algorithm framework for expensive problems

Attainment surfaces of the solutions with best (left) and worst (right) IGD values for various algorithms on UF1 problem. [Display omitted] •We studied the effect of local search on multi-objective memetic algorithm.•The base algorithm is single surrogate-based multi-objective memetic algorithm.•We...

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Veröffentlicht in:Applied soft computing Jg. 43; S. 1 - 19
Hauptverfasser: Palar, Pramudita Satria, Tsuchiya, Takeshi, Parks, Geoffrey Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2016
Schlagworte:
Expensive problems
Local search
Local surrogate
Memetic algorithm
Multi-objective
Memetic algorithm
Local search
Multi-objective
Local surrogate
Expensive problems
ISSN:1568-4946, 1872-9681
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Zusammenfassung:Attainment surfaces of the solutions with best (left) and worst (right) IGD values for various algorithms on UF1 problem. [Display omitted] •We studied the effect of local search on multi-objective memetic algorithm.•The base algorithm is single surrogate-based multi-objective memetic algorithm.•We investigated four types of local search.•Local search based on achievement-type function were more robust than the others.•The achievement-type function found better solutions on real-world problem. A comparative study of the impacts of various local search methodologies for the surrogate-assisted multi-objective memetic algorithm (MOMA) is presented in this paper. The base algorithm for the comparative study is the single surrogate-assisted MOMA (SS-MOMA) with the main aim being to solve expensive problems with a limited computational budget. In addition to the standard weighted sum (WS) method used in the original SS-MOMA, we studied the capabilities of other local search methods based on the achievement scalarizing function (ASF), Chebyshev function, and random mutation hill climber (RMHC) in various test problems. Several practical aspects, such as normalization and constraint handling, were also studied and implemented to deal with real-world problems. Results from the test problems showed that, in general, the SS-MOMA with ASF and Chebyshev functions was able to find higher-quality solutions that were more robust than those found with WS or RMHC; although on problems with more complicated Pareto sets SS-MOMA-WS appeared as the best. SS-MOMA-ASF in conjunction with the Chebyshev function was then tested on an airfoil-optimization problem and compared with SS-MOMA-WS and the non-dominated sorting based genetic algorithm-II (NSGA-II). The results from the airfoil problem clearly showed that SS-MOMA with an achievement-type function could find more diverse solutions than SS-MOMA-WS and NSGA-II. This suggested that for real-world applications, higher-quality solutions are more likely to be found when the surrogate-based memetic optimizer is equipped with ASF or a Chebyshev function than with other local search methods.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2015.12.039