Multi-objective non-permutation flow shop scheduling based on improved beluga whale optimization algorithm
Aiming at the problem of the low flexibility of the traditional single machining shop,a multi-objective improved beluga whale optimization(IBWO)algorithm was proposed to solve the problem.Firstly,a multi-objective non-permutation flow shop scheduling model was established to minimize the maximum completion time and minimize the energy consumption,according to the characteristics of the scheduling problem,a two-layer real number coding mechanism was designed to represent the solution of the problem.Secondly,the relationship between the advantages and disadvantages of the multi-objective solution was evaluated by using the non-dominant relation and crowding ranking algorithm,and the real number crossing and variable neighborhood search strategy were used to solve the problem.Finally,the IBWO algorithm was respectively compared with the beluga whale optimization(BWO)algorithm,the algorithm that using real number crossing but not using variable neighborhood search strategy(BWO-1),the algorithm that using variable neighborhood search strategy but not using real number crossing(BWO-2).Furthermore,the IBWO was compared with multi-objective optimization algorithms,such as non-dominated sorting genetic algorithm-Ⅱ(NSGA2),NSGA3 and strength Pareto evolutionary algorithm 2(SPEA2).The research results show that,the index of IBWO algorithm achieves a dominant position in at least 60%test cases by using the generational distance(GD),diversity index and inverted generational distance(IGD)to evaluate the comparative results.The IBWO algorithm using real number crossing and the variable neighborhood search strategy to make up for the shortcomings of the BWO algorithm's poor local search ability,effectively enhance the effectiveness and stability of the algorithm,improve the search ability of the improved algorithm.It can provide some guidances for the practical production.
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