Multi-objective Differential Evolution Algorithm Based on Affinity Propagation Clustering

Multi-objective problems have gained much attention during the last decade. To balance the diversity and the convergence of the multi-objective differential evolution algorithm (MODE), an improved MODE is proposed based on the affinity propagation clustering (APC) and the non-dominated count approac...

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Vydáno v:IAENG international journal of applied mathematics Ročník 53; číslo 4; s. 1 - 10
Hlavní autoři: Qu, Dan, Li, Hongyi, Chen, Huafei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hong Kong International Association of Engineers 01.12.2023
Témata:
Affinity
Archives & records
Clustering
Convergence
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Multiple objective analysis
Mutation
Optimization
Pareto optimization
Population distribution
Propagation
ISSN:1992-9978, 1992-9986
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Shrnutí:Multi-objective problems have gained much attention during the last decade. To balance the diversity and the convergence of the multi-objective differential evolution algorithm (MODE), an improved MODE is proposed based on the affinity propagation clustering (APC) and the non-dominated count approach in this paper. The proposed algorithm is referred to as АР-MODE, which improves the search efficiency by utilizing the affinity propagation approach to find out the population distribution structure for guiding search. In addition, mating restriction probability is used to select parent individuals for recombination from the neighborhoods or the whole population. Meanwhile, the mating restriction probability is updated according to the non-dominated count approach at each generation. This proposed algorithm is verified by comparing it with some state-of-the-art multi-objective evolutionary algorithms, and the simulation results on DTLZ test problems indicate that AP-MODE can efficiently achieve two goals of multi-objective optimization, i.e., the convergence to actual Pareto front and uniform spread of individuals along Pareto front.
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ISSN:1992-9978
1992-9986