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中文摘要：
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．
英文摘要：
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.
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