Multi-Population Optimization Framework Based on Plant Evolutionary Strategy and Its Application to Engineering Design Problems

Optimization problems are widespread across various fields, including industry, agriculture, and healthcare. Metaheuristic algorithms (MAs) are commonly employed to solve these problems due to their flexibility and robustness. However, despite their success, MAs inspired by plant evolutionary strate...

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Vydané v:	International journal of computational intelligence systems Ročník 18; číslo 1; s. 117 - 22
Hlavní autori:	Cheng, Hongwei, Li, Jun, Zhang, Xiaoming, Li, Tingjuan, Zhang, Panpan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 15.05.2025 Springer Nature B.V Springer
Predmet:	Adaptation Algorithms Artificial Intelligence Computational Intelligence Control Convergence Design Design engineering Design optimization Engineering Environmental conditions Evolution Exploitation Global optimization Heuristic methods Mathematical Logic and Foundations Mechatronics Metaheuristic algorithms Multi-population evolution Optimization algorithm framework Optimization algorithms Performance evaluation Plant population evolution Plant populations Research Article Robotics Robustness (mathematics) Seeds Strategy Task complexity Multi-population evolution Optimization algorithm framework Plant population evolution Metaheuristic algorithms
ISSN:	1875-6883, 1875-6891, 1875-6883
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Abstract	Optimization problems are widespread across various fields, including industry, agriculture, and healthcare. Metaheuristic algorithms (MAs) are commonly employed to solve these problems due to their flexibility and robustness. However, despite their success, MAs inspired by plant evolutionary strategies remain underexplored. This paper introduces a novel multi-population optimization framework based on the plant evolutionary strategy (PES_MPOF), which leverages plant evolutionary principles to improve optimization performance by maintaining population diversity and accelerating convergence in complex tasks. PES_MPOF integrates multiple subpopulations, each evolving according to different plant evolutionary models. These subpopulations mimic natural distribution and reproduction strategies, fostering solution diversity through both cooperation and competition. Additionally, PES_MPOF adapts population parameters based on the evolutionary performance of subpopulations, further enhancing its robustness and efficiency. The PES_MPOF algorithm was tested on the IEEE CEC 2020 benchmark suite and several classic engineering design problems. It outperforms other state-of-the-art optimization algorithms, demonstrating significant improvements in global optimization, solution accuracy, and convergence speed. PES_MPOF effectively addresses the challenges of premature convergence and loss of diversity, making it a robust and efficient optimization tool. Its innovative multi-population framework, inspired by plant evolutionary strategies, enhances both exploration and exploitation. Experimental results validate its effectiveness across a broad range of optimization problems, including those with constraints. The part of algorithm’s code will be made available upon the paper’s acceptance: https://github.com/ChengHongwei430/PES_MPOF .
AbstractList	Optimization problems are widespread across various fields, including industry, agriculture, and healthcare. Metaheuristic algorithms (MAs) are commonly employed to solve these problems due to their flexibility and robustness. However, despite their success, MAs inspired by plant evolutionary strategies remain underexplored. This paper introduces a novel multi-population optimization framework based on the plant evolutionary strategy (PES_MPOF), which leverages plant evolutionary principles to improve optimization performance by maintaining population diversity and accelerating convergence in complex tasks. PES_MPOF integrates multiple subpopulations, each evolving according to different plant evolutionary models. These subpopulations mimic natural distribution and reproduction strategies, fostering solution diversity through both cooperation and competition. Additionally, PES_MPOF adapts population parameters based on the evolutionary performance of subpopulations, further enhancing its robustness and efficiency. The PES_MPOF algorithm was tested on the IEEE CEC 2020 benchmark suite and several classic engineering design problems. It outperforms other state-of-the-art optimization algorithms, demonstrating significant improvements in global optimization, solution accuracy, and convergence speed. PES_MPOF effectively addresses the challenges of premature convergence and loss of diversity, making it a robust and efficient optimization tool. Its innovative multi-population framework, inspired by plant evolutionary strategies, enhances both exploration and exploitation. Experimental results validate its effectiveness across a broad range of optimization problems, including those with constraints. The part of algorithm’s code will be made available upon the paper’s acceptance: https://github.com/ChengHongwei430/PES_MPOF. Optimization problems are widespread across various fields, including industry, agriculture, and healthcare. Metaheuristic algorithms (MAs) are commonly employed to solve these problems due to their flexibility and robustness. However, despite their success, MAs inspired by plant evolutionary strategies remain underexplored. This paper introduces a novel multi-population optimization framework based on the plant evolutionary strategy (PES_MPOF), which leverages plant evolutionary principles to improve optimization performance by maintaining population diversity and accelerating convergence in complex tasks. PES_MPOF integrates multiple subpopulations, each evolving according to different plant evolutionary models. These subpopulations mimic natural distribution and reproduction strategies, fostering solution diversity through both cooperation and competition. Additionally, PES_MPOF adapts population parameters based on the evolutionary performance of subpopulations, further enhancing its robustness and efficiency. The PES_MPOF algorithm was tested on the IEEE CEC 2020 benchmark suite and several classic engineering design problems. It outperforms other state-of-the-art optimization algorithms, demonstrating significant improvements in global optimization, solution accuracy, and convergence speed. PES_MPOF effectively addresses the challenges of premature convergence and loss of diversity, making it a robust and efficient optimization tool. Its innovative multi-population framework, inspired by plant evolutionary strategies, enhances both exploration and exploitation. Experimental results validate its effectiveness across a broad range of optimization problems, including those with constraints. The part of algorithm’s code will be made available upon the paper’s acceptance: https://github.com/ChengHongwei430/PES_MPOF . Abstract Optimization problems are widespread across various fields, including industry, agriculture, and healthcare. Metaheuristic algorithms (MAs) are commonly employed to solve these problems due to their flexibility and robustness. However, despite their success, MAs inspired by plant evolutionary strategies remain underexplored. This paper introduces a novel multi-population optimization framework based on the plant evolutionary strategy (PES_MPOF), which leverages plant evolutionary principles to improve optimization performance by maintaining population diversity and accelerating convergence in complex tasks. PES_MPOF integrates multiple subpopulations, each evolving according to different plant evolutionary models. These subpopulations mimic natural distribution and reproduction strategies, fostering solution diversity through both cooperation and competition. Additionally, PES_MPOF adapts population parameters based on the evolutionary performance of subpopulations, further enhancing its robustness and efficiency. The PES_MPOF algorithm was tested on the IEEE CEC 2020 benchmark suite and several classic engineering design problems. It outperforms other state-of-the-art optimization algorithms, demonstrating significant improvements in global optimization, solution accuracy, and convergence speed. PES_MPOF effectively addresses the challenges of premature convergence and loss of diversity, making it a robust and efficient optimization tool. Its innovative multi-population framework, inspired by plant evolutionary strategies, enhances both exploration and exploitation. Experimental results validate its effectiveness across a broad range of optimization problems, including those with constraints. The part of algorithm’s code will be made available upon the paper’s acceptance: https://github.com/ChengHongwei430/PES_MPOF .
ArticleNumber	117
Author	Li, Jun Zhang, Xiaoming Cheng, Hongwei Zhang, Panpan Li, Tingjuan
Author_xml	– sequence: 1 givenname: Hongwei surname: Cheng fullname: Cheng, Hongwei organization: Institutes of Physical Science and Information Technology, Anhui University – sequence: 2 givenname: Jun surname: Li fullname: Li, Jun organization: Institutes of Physical Science and Information Technology, Anhui University – sequence: 3 givenname: Xiaoming surname: Zhang fullname: Zhang, Xiaoming email: iimzxm@gmail.com organization: Institutes of Physical Science and Information Technology, Anhui University – sequence: 4 givenname: Tingjuan surname: Li fullname: Li, Tingjuan organization: Qinghai Institute of Science and Technology Information – sequence: 5 givenname: Panpan surname: Zhang fullname: Zhang, Panpan organization: School of Electrical Engineering, Xi’an University of Technology
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SubjectTerms	Adaptation Algorithms Artificial Intelligence Computational Intelligence Control Convergence Design Design engineering Design optimization Engineering Environmental conditions Evolution Exploitation Global optimization Heuristic methods Mathematical Logic and Foundations Mechatronics Metaheuristic algorithms Multi-population evolution Optimization algorithm framework Optimization algorithms Performance evaluation Plant population evolution Plant populations Research Article Robotics Robustness (mathematics) Seeds Strategy Task complexity
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Title	Multi-Population Optimization Framework Based on Plant Evolutionary Strategy and Its Application to Engineering Design Problems
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