Evolving better population distribution and exploration in evolutionary multi-objective optimization

The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimizati...

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Vydáno v:European journal of operational research Ročník 171; číslo 2; s. 463 - 495
Hlavní autoři: Tan, K.C., Goh, C.K., Yang, Y.J., Lee, T.H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.06.2006
Elsevier
Elsevier Sequoia S.A
Edice:European Journal of Operational Research
Témata:
Applied sciences
Comparative analysis
Evolutionary computations
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Global optimization
Multiple criteria analysis
Multiple criteria decision making
Mutation
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Optimization algorithms
Pareto optimum
Studies
Evolutionary computations
Global optimization
Multiple criteria analysis
Pareto optimum
Genetic algorithm
Multicriteria analysis
Evolutionary computation
Multiobjective programming
Global optimum
Optimization
ISSN:0377-2217, 1872-6860
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Shrnutí:The aim of multi-objective evolutionary optimization is to minimize the distance between the solution set and the true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the optimization ability of multi-objective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is an enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. The strategy also preserves non-dominated solutions in the evolving population to achieve a good convergence for the optimization. Comparative studies of some well-known diversity operators, mutation operators and multi-objective evolutionary algorithms are performed on different benchmark problems, which illustrate the effectiveness and efficiency of the proposed features.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2004.08.038