改进蛙跳算法的约束处理方法

Constraint handling method based on shuffled frog leaping algorithm

提出了一种用于求解有约束优化问题的混合蛙跳算法.蛙跳算法结合ε-差分进化算法(ε-differential evolution algorithm,ε-DE),可使算法在进化过程中充分利用种群中不可行解的信息.在进化初始阶段,可行域边界上拥有较优目标函数的不可行解进入种群,随着进化代数增加,种群约束允许放松程度不断减小,使得种群中不可行解数量减少,直到种群约束允许放松程度为0,此时种群完全由可行解组成.改进后的蛙跳算法能够提高收敛速度和精度.13个标准Benchmark函数仿真试验的结果表明,改进后的蛙跳算法寻优精度高,鲁棒性强,是一种有效的求解有约束优化问题的算法.

An improved shuffled frog leaping algorithm （SFLA） to solving constrained optimization problems was proposed. Combined with ε- differential evolution algorithm （ ε - DE） , the infeasible solutions with better objective function were made full use of in the evolution process. In the initial stage of evolution, the infeasible solutions with better objective function and near the boundary of the feasible region were incorporated in the population. With the evolutionary generation increasing, the decrease in the population constraint relax degree decreased the number of infeasible solutions in the population. Until the population constraint relax degree was 0, the population was entirely composed of feasible solutions. The convergence speed and accuracy had been greatly improved by the improved algorithm. The simulation results of 13 Benchmark functions proved that the algorithm had higher precision and stronger robustness, was a very effective algorithm to solve the constrained optimization problems.