Chaos-based improved marine predators algorithm for flexible job-shop scheduling problem.

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Bibliographic Details
Title:	Chaos-based improved marine predators algorithm for flexible job-shop scheduling problem.
Authors:	Zhang, Yongping¹ (AUTHOR) zhyp916@163.com, Yao, Xiong¹ (AUTHOR), Xu, Sen¹ (AUTHOR)
Source:	Journal of Mechanical Science & Technology. Oct2024, Vol. 38 Issue 10, p5581-5594. 14p.
Subjects:	Metaheuristic algorithms, Grey Wolf Optimizer algorithm, Production scheduling, Combinatorial optimization, Ant algorithms, Chaos theory, Deterministic algorithms
Abstract:	The marine predators algorithm (MPA) is a new metaheuristic optimization algorithm for solving continuous optimization problems. This study proposes the chaos-based improved MPA (CIMPA) for solving the flexible job-shop scheduling problem (FJSP). In CIMPA, we use the Sobol sequence generated by a deterministic algorithm as the initial population to cover the solution space extensively, introduce the opposition-based learning method to increase the diversity of the initial population and improve the search efficiency of populations, design a combinatorial operator scheme based on chaos theory to enhance the global exploration ability, and define a nonlinear step-size parameter, i.e., CF, to balance global exploration and local exploitation. Finally, to verify the ability of CIMPA in solving FJSP, we adopt the smallest position value method to achieve the mutual conversion of a prey's continuous position vectors and discrete scheduling solutions. In accordance with the experimental results of 43 standard examples of FJSP, CIMPA achieves 31 optimal values (the optimal solutions are used as the benchmark), 34 optimal values (the average solutions are used as the benchmark), and 35 optimal values (the standard deviations are used as the benchmark) compared with classical algorithms, such as ant colony algorithm, whale optimization algorithm, gray wolf optimizer, and their improved versions. These results show that CIMPA exhibits better optimization performance than classical algorithms when applied to FJSP, verifying the effectiveness and feasibility of CIMPA in solving the discreteness problem. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	The marine predators algorithm (MPA) is a new metaheuristic optimization algorithm for solving continuous optimization problems. This study proposes the chaos-based improved MPA (CIMPA) for solving the flexible job-shop scheduling problem (FJSP). In CIMPA, we use the Sobol sequence generated by a deterministic algorithm as the initial population to cover the solution space extensively, introduce the opposition-based learning method to increase the diversity of the initial population and improve the search efficiency of populations, design a combinatorial operator scheme based on chaos theory to enhance the global exploration ability, and define a nonlinear step-size parameter, i.e., CF, to balance global exploration and local exploitation. Finally, to verify the ability of CIMPA in solving FJSP, we adopt the smallest position value method to achieve the mutual conversion of a prey's continuous position vectors and discrete scheduling solutions. In accordance with the experimental results of 43 standard examples of FJSP, CIMPA achieves 31 optimal values (the optimal solutions are used as the benchmark), 34 optimal values (the average solutions are used as the benchmark), and 35 optimal values (the standard deviations are used as the benchmark) compared with classical algorithms, such as ant colony algorithm, whale optimization algorithm, gray wolf optimizer, and their improved versions. These results show that CIMPA exhibits better optimization performance than classical algorithms when applied to FJSP, verifying the effectiveness and feasibility of CIMPA in solving the discreteness problem. [ABSTRACT FROM AUTHOR]
ISSN:	1738494X
DOI:	10.1007/s12206-024-0929-8