求解约束优化问题M-精英协同进化算法

doi:10.3969/j.issn.1001-2400.2010.05.014

Abstract

Abstract:

An algorithm named the M-Elite Coevolutionary Algorithm (MECA) is proposed for constrained optimization problems. The algorithm simulates the establishment and cooperation of teams in the human society with emphasis on the important role of elites in teams. The whole population is divided into two subpopulations, i.e., elite population and common population, with the former leading the latter to evolve so as to accelerate the evolution of the whole population. During this process several teams are established, each elite individual acting as the core of each team. The cooperating operation is implemented between the different elite individuals, and the common individuals are led by the elite individuals in the leading operation during the process of team establishment. The above cooperating operation and the leading operation are defined by the different combinations of several crossover operators or mutation operators. Static penalty functions are used to transform a constrained optimization problem into an unconstrained one, and tests are made on 13 benchmark problems. Experimental results and parameter analysis show that the MECA can obtain a high solution quality with less runtime and is robust and that it obtains better performances than Organizational Evolutionary Algorithm, and can solve difficult constrained optimization problem.

Key words: optimization algorithms, constrained optimization, evolutionary algorithms, coevolutionary algorithms

MU Cai-hong;JIAO Li-cheng;LIU Yi. M-Elite coevolutionary algorithm for constrained optimization[J].J4, 2010, 37(5): 852-861.

References

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M-Elite coevolutionary algorithm for constrained optimization

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