The circular discrete particle swarm optimization algorithm for flow shop scheduling problem

Zhang, Jindong; Zhang, Changsheng; Liang, Shubin

doi:10.1016/j.eswa.2010.02.024

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…Popular swarm intelligence metaheuristics that have been used to solve the flow shop problem include ant colony optimization (ACO) (Blum, 2005), particle swarm optimization (PSO) (Zhang et al, 2010), bee colony optimization (BCO) (Huang & Lin, 2011), and the artificial fish swarm algorithm (AFSA) (Babaee et al, 2020). In addition to swarm intelligence algorithms, other types of metaheuristics have also been proposed and applied to solve the flow shop problem.…”

Section: Related Workmentioning

confidence: 99%

Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making

Mzili¹,

Mzili²,

Riffi³

2023

Decis. Mak. Appl. Manag. Eng.

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The Rat Swarm Optimizer (RSO) algorithm is examined in this paper as a potential remedy for the flow shop issue in manufacturing systems. The flow shop problem involves allocating jobs to different machines or workstations in a certain order to reduce execution time or resource use. The objective function is used by the RSO method to optimize the results after mapping the rat locations to task-processing sequences. The RSO method successfully locates high-quality solutions to the flow shop problem when compared to other metaheuristic algorithms on diverse test situations. This research helps to improve the flexibility, lead times, quality, and efficiency of the production system. The paper introduces the RSO algorithm, creates a mapping strategy, redefines mathematical operators, suggests a method to enhance the quality of solutions, shows how successful the algorithm is through simulations and comparisons, and then uses statistical analysis to confirm the algorithm's performance.

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Section: Related Workmentioning

confidence: 99%

Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making

Mzili¹,

Mzili²,

Riffi³

2023

Decis. Mak. Appl. Manag. Eng.

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“…In the context of the FSSP [9], these algorithms encode potential solutions as particles, ants or bees and iteratively explore the solution space by updating the position or path of these agents based on their own experiences and the collective knowledge of the swarm. The agents collaborate and compete, allowing the swarm to converge towards an optimal or near-optimal solution for the makespan.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

Mzili

Riffi

2023

Algorithms

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This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena algorithm (SHOA) to solve the production shop scheduling problem. The proposed GA-SHOA algorithm incorporates genetic operators, such as uniform crossover and mutation, into the SHOA algorithm to improve its performance. We evaluated the algorithm on a set of OR library instances and compared it to other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO and ACGA. The experimental results show that the GA-SHOA algorithm consistently finds optimal or near-optimal solutions for all tested instances, outperforming the other algorithms. Our paper contributes to the field in several ways. First, we propose a hybrid algorithm that effectively combines the exploration and exploitation capabilities of SHO and GA, resulting in a balanced and efficient search process for finding near-optimal solutions for the FSSP. Second, we tailor the SHO and GA methods to the specific requirements of the FSSP, including encoding schemes, objective function evaluation and constraint handling, which ensures that the hybrid algorithm is well suited to address the challenges posed by the FSSP. Third, we perform a comprehensive performance evaluation of the proposed hybrid algorithm, demonstrating its effectiveness in terms of solution quality and computational efficiency. Finally, we provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process, which can help understand the factors contributing to the success of the algorithm and provide insight into potential improvements or adaptations to other combinatorial optimization problems.

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“…On one hand, the selection of inertia section and acceleration coefficients has a significant effect on the performance in the motion equation. However, some selection experiences in other fields [8][9][10][11][12] may not be suitable for the CAP. On the other hand, the scheme of frequency exhaust assignment (FEA), a call orderings based strategy, can firstly assign channels to the most difficult cells.…”

Section: Introductionmentioning

confidence: 99%

Whether and How to Select Inertia and Acceleration of Discrete Particle Swarm Optimization Algorithm: A Study on Channel Assignment

Jin

Zhong

Zhao

2014

Mathematical Problems in Engineering

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There is recently a great deal of interest and excitement in understanding the role of inertia and acceleration in the motion equation of discrete particle swarm optimization (DPSO) algorithms. It still remains unknown whether the inertia section should be abandoned and how to select the appropriate acceleration in order for DPSO to show the best convergence performance. Adopting channel assignment as a case study, this paper systematically conducts experimental filtering research on this issue. Compared with other channel assignment schemes, the proposed scheme and the selection of inertia and acceleration are verified to have the advantage to channel assignment in three respects of convergence rate, convergence speed, and the independency of the quality of initial solution. Furthermore, the experimental result implies that DSPO might have the best convergence performance when its motion equation includes an inertia section in a less medium weight, a bigger acceleration coefficient for global-search optimum, and a smaller acceleration coefficient for individual-search optimum.

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The circular discrete particle swarm optimization algorithm for flow shop scheduling problem

Cited by 21 publications

References 21 publications

Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making

Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making

Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

Whether and How to Select Inertia and Acceleration of Discrete Particle Swarm Optimization Algorithm: A Study on Channel Assignment

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