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

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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]
Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Chaos-based improved marine predators algorithm for flexible job-shop scheduling problem.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Oct2024, Vol. 38 Issue 10, p5581-5594. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Chaos+theory%22">Chaos theory</searchLink><br /><searchLink fieldCode="DE" term="%22Deterministic+algorithms%22">Deterministic algorithms</searchLink>
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  Data: 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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  Data: <i>Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s12206-024-0929-8
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        Type: general
      – SubjectFull: Grey Wolf Optimizer algorithm
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      – SubjectFull: Production scheduling
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      – SubjectFull: Chaos theory
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              Text: Oct2024
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