基于正态分布和自适应变异算子的ε截断算法

针对约束优化算法不能很好协调收敛性及分布性的问题,提出一种基于正态分布和自适应变异算子的ε截断算法。将正态分布引入模拟二进制交叉算子中,使算法可搜索的空间范围更广,更易跳出局部最优;利用自适应变异算子,将种群个体当前信息与变异算子结合起来,引导种群向真实的Pareto前沿进行进化;结合自适应的ε截断策略,保留Pareto最优解和一定数量的不可行解,同时利用不可行解的信息,加大对搜索空间的探索力度,从而提高种群多样性。采用3种标准测试函数对算法进行测试,试验结果表明:本研究所求解集能够很好的跟踪真实的Pareto解集。该方法可以有效地协调算法的收敛性及分布性。

Epsilon truncation algorithm based on NDX and adaptive mutation operator

It was hard for constrained optimization algorithm to maintain a good balance of convergence and distribution. To solve this problem, a ε-truncation algorithm based on the normal distribution crossover (NDX) and adaptive mutation operator was proposed, which introduced the normal distribution into the simulated binary crossover (SBX) operator, so that the algorithm could search wider space and easily jump out of local optimum. The proposed algorithm combined the adaptive mutation operator with the current information of the individual in the population, and guided the population evolving toward the real Pareto front. Moreover, it preserved the Pareto optimal solutions and a certain number of infeasible solutions with the adaptive ε truncation strategy. At the same time using the information of these infeasible solutions, it increased the search intensity of space and improved the diversity of population. According to three standard test functions experimental results, the solution set in this study could well track the real Pareto solution set. The proposed method could effectively coordinate the convergence and distribution of the algorithm.