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