欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
对于约束优化问题，目前提出的差分进化算法大多采用罚函数法，但此方法对罚参数有很强的依赖性．基于此，把约束优化问题中的约束条件当作一个目标函数，从而把约束优化问题转化为有两个目标函数的多目标优化问题．借鉴多目标优化中的Pareto的概念，对种群中的个体规定等级，便于在优胜劣汰过程中确定选择概率．同时，在算法陷入局部最优时，采用一种不可行解替换机制来提高算法搜索能力．对13个标准测试问题的测试结果表明，与动态惩罚函数的进化算法、可行性规则的差分进化算法、采用随机排序的进化策略以及人工免疫响应约束进化策略相比，新算法在求解精度上均具有一定的优势．
英文摘要：
Most existing differential evolution algorithms for the Constrained Optimization Problem（COP） use the penalty function method to handle constrains, which depends strongly on the penalty parameter. So, this paper transforms the COP into two-objective multi-objective optimization by taking constraints as an objective function. Based on the concept of Pareto, the grades of individuals in population are prescribed so as to determine their selection probability in the process of ＂survival of the fittest＂. In addition, when the algorithm gets into a local optimum, an infeasible solution replacing mechanism is also given to improve the search capability. The results of the 13 Standard tests show that compared to the Evolutionary Algorithm based on Homomorphous Maps（EAHM）, Constraint Handling Differential Evolution （CHDE）, Evolutionary Strategies based on Stochastic Ranking （ESSR） and Artificial Immune Response Constrained Evolutionary Strategy （AIRCES）, the proposed algorithm has certain advantages in convergence speed and solution accuracy.
同期刊论文项目
同项目期刊论文
位置：