基于混合杂交与间歇变异的约束优化演化算法

In solving constrained optimization problems with genetic algorithms, more emphases are laid on handling constraints than increasing the search capability of algorithms, which often leed to unsatisfied results as reported inmost literatures. This paper proposes a new evolutionary algorithm for constrained optimization, emphasizing moreon increasing the search capability of the algorithm by means of hybrid crossovers and intermittent mutation while adopting a simple constraint handling technique called direct comparison. Numerical experiments and comparisons show the ettectiveness of the proposed algorithm.

A Constrained Evolutionary Algorithm Based on Hybrid Crossovers and Intermittent Mutation

In solving constrained optimization problems with genetic algorithms, more emphases are laid on handling constraints than increasing the search capability of algorithms, which often leed to unsatisfied results as reported in most literatures. This paper proposes a new evolutionary algorithm for constrained optimization, emphasizing more on increasing the search capability of the algorithm by means of hybrid crossovers and intermittent mutation while adopting a simple constraint handling technique called direct comparison. Numerical experiments and comparisons show the effectiveness of the proposed algorithm.