Hybrid Genetic Algorithm for Solving Non-Linear Programming Problem
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摘要: 面向非线性规划问题,通过引入准可行方向、准可行方向的可行度等新概念,提出了 描述和度量非可行点(染色体)的新方法;通过嵌入非可行染色体的信息于评价函数中,突破 传统的给非可行染色体以大的惩罚的思想,提出了三种新的评价非可行染色体的方法.基于 梯度方向搜索和新的评价方法,提出了一种新的沿权重梯度方向变异的混合式遗传算法 (HGA).对测试问题的仿真结果表明了算法的有效性.Abstract: Based on the introduction of the new concepts of semi-feasible direction, feasible degree of semi-feasible direction, feasible degree of illegal points 'belonging to' feasible domain, etc. this paper proposes a new method for formulating and evaluating illegal points and three new kinds of evaluation functions, and develops a special hybrid genetic algorithm (HGA) with penalty function and weighted gradient direction search for non linear programming problems. Simulation shows that this method is effective.
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