Competitive cooperation for strategy adaptation in coevolutionary genetic algorithm for constrained optimization

A coevolutionary algorithm as a search strategy adaptation procedure in constrained optimization is discussed in the paper. The coevolutionary algorithm consists of the set of individual conventional genetic algorithms with different search strategies. Individual genetic algorithms compete and cooperate with each other. Competition is provided with resource re-allocation among algorithms and cooperation is provided with migration of the best individuals to all of the algorithms. At early works this method was applied for unconstrained optimization problems. The common result was that coevolutionary algorithm is more effective than average individual genetic algorithms. In this paper modification of competitive-cooperative coevolutionary algorithm for constrained optimization problems is considered. Results of test comparison of coevolutionary algorithm with conventional genetic algorithms demonstrate that coevolutionary algorithm is not less effective than the best for problem-in-hand individual conventional algorithm.