一种求解双目标flow shop排序问题的进化算法

提出一种求解双目标flow shop排序的递进多目标进化算法.算法采用改进的精英复制策略,在实现精英保留的前提下降低了计算复杂性;通过递进进化模式增加群体多样性,改善了算法收敛性;通过群体进化过程中对非劣解集进行竞争型可变邻域启发式搜索,增强了算法局部搜索性能.采用新算法和参照算法NSGA-II对31个标准双目标flow shop算例进行优化.研究结果表明,新算法在所有算例的求解中均获得了优于NSGA-II的非劣解集,验证了算法的有效性.

Escalating evolutionary algorithm with application to bi-objective flow shop scheduling problems

An escalating multi-objective evolutionary algorithm(EMEA),which aims at solving bi-objective flow shop scheduling problem,is proposed in this paper.The new algorithm takes a new elite duplication strategy and an innovative escalating evolutionary structure,which improved the convergence and efficiency of the algorithm and reduced its computational cost.Besides,the proposed algorithm combines those meta-heuristic algorithms,which are adept at solving specific objective optimization with flow shop scheduling problems, into a tournament variable Pareto local search strategy at the end of each generation.31 typical bi-objective flow shop case studies have been employed for demonstration.The optimization results have shown that,EMEA has gotten outstanding Pareto frontiers in all test problems by contrast to those of a well-known algorithm NSGA-II,which revealed its efficiency and effectiveness in solving bi-objective flow shop scheduling problems.