【摘要】 约束优化问题最优解通常分布在可行域边界上或在可行域边界附近,对其求解比较困难。对此类问题提出了一种基于D.S.C(.Davies,Swann,Campey)法的混合进化算法,简记为I.D.S.C。从某个随机解出发,利用改进了停止法则的D.S.C.法进行一维搜索,使得搜索到的解在可行域边界或其附近;采用了新的适应度函数,可以自动选择有潜力的解,无需像类似方法分情况进行选择;同时,为了避免丢掉好的解,算法还启用了保留一定数目可行解的策略。对5个标准的测试函数进行了实验,结果验证了算法的有效性。更多还原

【Abstract】 The global solutions of the constrained optimization problems often locate on or are near the boundary of the feasible region, and it is hard to get the global solutions. A novel hybrid evolutionary algorithm based on Davies, Swann and Campey search method(D.S.C.), referred as Improved D.S.C.(I.D.S.C.)is proposed. Starting from a random solution, a solution on or near the boundary of the feasible region is got by D.S.C.search method with the revised stop criterion. Then, a new fitness function is used. It can automatically select potential high quality solutions without discussion of the different situations as the similar fitness did; meanwhile, avoid throwing away potential high quality solutions, the strategy of keeping a certain number of feasible solutions is adopted. At last, the computer simulations are made on 5 benchmark problems and the results demonstrate that the proposed algorithm is effective.更多还原