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A Multipopulation-Based Multiobjective Evolutionary Algorithm
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DOI：10.1109/TCYB.2018.2871473.png)
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Abstract
En 中文
Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance.
Keywords:
Sociology
Statistics
Optimization
Mathematical model
Genetic algorithms
Evolutionary computation
Markov processes
Evolutionary algorithm
genetic algorithms (GAs)
Markov chain
multiobjective optimization
multipopulation
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