一种新的自适应惩罚函数算法求解约束优化问题

A New Adaptive Penalty Function Based Algorithm for Solving Constrained Optimization Problems

  • 摘要: 提出一种新的自适应惩罚函数法,用来处理约束优化问题.这种方法根据当前群体中可行解的比例对目标函数和违反约束条件的程度作出合适的权衡,具有结构简单、参数少等优点.把它和一个简单的进化策略结合起来,得到了一种新的求解约束优化问题的进化算法.选取几个常见的测试函数对这种新方法进行了数值实验.结果表明,所提方法能够非常有效地处理各种约束优化问题,而且具有很强的稳健性;其性能优于或相似于一些尖端的算法.

     

    Abstract: A new adaptive penalty function is presented to solve constrained optimization problems.With a simple structure and fewer parameters,the method appropriately balances the objective function and the constraint violations according to the proportion of feasible solutions in the current population.By integrating the presented method with a simple evolutionary strategy,a novel constrained optimization evolutionary algorithm is derived.The new method is tested on several benchmark test functions.The experimental results show that the new method is an effictive and robust approach for solving different constrained optimization problems,and it outperforms or performs similarly to other algorithms.

     

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