Quantum-inspired cuckoo co-search algorithm for no-wait flow shop scheduling

Zhu, Haihong; Qi, Xianbiao; Chen, Fulong; He, Xin; Chen, Linfeng; Zhang, Ziyang

doi:10.1007/s10489-018-1285-0

Cited by 22 publications

(10 citation statements)

References 35 publications

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“…Moreover, they derived an index priority rule for two machine NW-FSS with the minimum weighted total completion time criterion. Zhu et al [26] presented a novel quantum-inspired cuckoo co-search algorithm for solving the NW-FSS problem with the makespan criterion. Allahverdi and Allahverdi [27] developed a two-machine NW-FSS problem with the total completion time performance criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

Keskin

Engin

2021

SN Appl. Sci.

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This paper addresses the m-machine no-wait Flow Shop Scheduling with Setup Times (NW-FSSWST). Two performance measures: total flow time and makespan are considered. The objective is to find a sequence that minimizing total flow time ($$\sum C_{j}$$ ∑ C j ) and makespan ($$C_{j}$$ C j ) simultaneously. A Hybrid Genetic Local and Global Search Algorithm (HGLGSA) is proposed to solve the NW-FSSWST for two performance criteria. The hybrid genetic algorithm is constructed by insert-search and self-repair algorithm with self-repair function. The proposed HGLGSA is tested on 192 benchmark problems of NW-FSSWST in the literature. A full factorial experimental design is made for determined the best parameter sets that improve the performance of the proposed algorithm. The computational results are compared with the benchmark solutions from the literature. The experimental results demonstrate the effectiveness and efficiency of the proposed HGLGSA for solving NW-FSSWST.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

Keskin

Engin

2021

SN Appl. Sci.

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“…Results indicate the reasonable computational time for large size problems. In addition to above studies, it is worth mentioning that some excellent algorithms have been presented for solving combinatorial optimization problems such as [74][75][76][77], and some of them are specialized in the field of scheduling, for instance [78][79][80][81], and which all of them give us valuable insight to extend our algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Salimifard

Mohammadi

et al. 2020

Appl Intell

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Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms.

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“…obtained some research results. According to the mechanism and technology used, the progress and attempts made by CS can be divided into the following categories: (1) research on the improvement of position update mechanism of CS [12], [17]- [23]; (2) improvement of the parameter adjustment method of CS [17], [18], [20]- [28]; (3) application research of CS [3], [8], [25], [29]- [40]. The improvement and research of these algorithms not only improve the optimization performance of CS, but also promote the development of CS theory and application.…”

Section: Many Scholars Have Conducted In-depth Research On Cs Andmentioning

confidence: 99%

“…Therefore, it is easier to implement. At present, CS has been widely used in travel sales problem [2]- [4], job scheduling [5]- [8], location problem [9], [10], fault diagnosis and prediction [11], [12] and image processing and classification [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm

et al. 2019

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Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.

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Quantum-inspired cuckoo co-search algorithm for no-wait flow shop scheduling

Cited by 22 publications

References 35 publications

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm

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