求解约束优化问题的内外交叉遗传算法

针对很多约束优化问题的最优解位于可行域的边界上或其附近的特点,提出了一种新的遗传算法.算法将种群中的可行解和不可行解分别存贮在两个容器中,新设计的交叉算子(内外交叉法)尽量让可行域内的可行解与可行域外的不可行解交叉,并顺着有利的方向一维搜索到可行域边界,此举既增大了个体接近全局最优解的几率, 又增强了算法的收敛速度;粒子群变异法则吸取粒子群 (PSO)算法的优点,让粒子沿粒子自身历史最优和全局最优的方向变异, 而选择算子则采取了保留固定比例不可行解的方法. 仿真结果证明了算法能够在种群规模小,迭代次数少的情况下迅速接近或找到全局最优解.

Inward-outward crossover based genetic algorithm for constrained optimization problem

Considering that the global optimal solutions often locate on or near the boundary of the feasible region for many constrained optimization problems,a novel genetic algorithm was proposed in this paper.The basic idea of the proposed algorithm was to put feasible solutions and infeasible solutions into two different containers respectively.Subsequently,a new designed crossover operator(named inwardoutward crossover operator)was used to a feasible solution and a infeasible solution,then a line search along a potential decent direction was used to improve the offspring so as to find a good solution on or near to the boundary of feasible region.By this search procedure,the possibility for obtaining the globally optimal solution is obviously enhanced,and similarly,the convergent speed is also strengthened.The"particle swarm mutation"inherited the advantages of Particle Swarm Optimization(PSO)algorithm and searched for the potential solution along the direction of the best current particle and the direction of the best individual of the whole swarm in the past.Selection operator retained a constant rate of infeasible solutions.Numerical results indicate that the proposed algorithm can be efficient to get global optimal solutions or near to them in smaller population and less iteration times.