A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

•A Discrete Water Wave Optimization (DWWO) Algorithm is proposed.•An Improved Iterated greedy algorithm is integrated into the framework of DWWO.•A modified initialization strategy is proposed to generate the initial population.•A ruling out inferior solution mechanism is added to improve the conver...

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Veröffentlicht in:Expert systems with applications Jg. 91; S. 347 - 363
Hauptverfasser: Zhao, Fuqing, Liu, Huan, Zhang, Yi, Ma, Weimin, Zhang, Chuck
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.01.2018
Elsevier BV
Schlagworte:
Breaking
Combinatorial analysis
Convergence
Effectiveness
Global optimization
Greedy algorithms
Iterated greedy algorithm
Job shop scheduling
Makespan
Markov chains
No-wait flow shop scheduling problem
Operators
Optimization algorithms
Performance assessment
Refraction
Scheduling algorithms
Searching
Shallow water
Studies
Water Wave Optimization (WWO)
Wave propagation
Iterated greedy algorithm
Makespan
Water Wave Optimization (WWO)
No-wait flow shop scheduling problem
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•A Discrete Water Wave Optimization (DWWO) Algorithm is proposed.•An Improved Iterated greedy algorithm is integrated into the framework of DWWO.•A modified initialization strategy is proposed to generate the initial population.•A ruling out inferior solution mechanism is added to improve the convergence speed.•The convergence of the DWWO algorithm has been proved theoretically. In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2017.09.028