文献类型：期刊文献

英文题名：An improved gravitational search algorithm to the hybrid flowshop with unrelated parallel machines scheduling problem

作者：Cao, Cuiwen[1]; Zhang, Yao[1]; Gu, Xingsheng[1]; Li, Dan[2]; Li, Jie[2]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China; [2] Centre for Process Integration, Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, United Kingdom

年份：2020

起止页码：1

外文期刊名：International Journal of Production Research

收录：EI(收录号：20202908940361)

语种：英文

外文关键词：Manufacture - Scheduling - Learning algorithms - Combinatorial optimization - Particle swarm optimization (PSO)

摘要：The hybrid ?owshop scheduling problem with unrelated parallel machines exists in many industrial manufacturers, which is an NP-hard combinatorial optimisation problem. To solve this problem more effectively, an improved gravitational search (IGS) algorithm is proposed which combines three strategies: generate new individuals using the mutation strategy of the standard differential evolution (DE) algorithm and preserve the optimal solution via a greedy strategy; substitute the exponential gravitational constant of the standard gravitational search (GS) algorithm with a linear function; improve the velocity update formula of the standard GS algorithm by mixing an adaptive weight and the global search strategy of the standard particle swarm optimisation (PSO) algorithm. Benchmark examples are solved to demonstrate the proposed IGS algorithm is superior to the standard genetic algorithm, DE, GS, DE with local search, estimation of distribution algorithm and artificial bee colony algorithms. Two more examples from a real-world water-meter manufacturing enterprise are effectively solved. ? 2020, ? 2020 Informa UK Limited, trading as Taylor & Francis Group.