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<p>This paper aims to develop a novel variant of the moth swarm algorithm (MSA) for addressing nonlinearity coupled and complex flexible AC transmission systems integrated optimal power flow problems for two standard IEEE test systems. The MSA is a swarm intelligence-based technique that imitates the moth’s navigational strategy towards moonlight. Despite its simple algorithmic structure and ability to explore a large search space for better solutions, the MSA has some drawbacks, including a sluggish convergence rate, insufficient intensification, and high chances of getting entrapped in the sub-optimal solution point. To address the limitations of the orthodox MSA, this paper devises a chaos-incorporated partial opposition-based MSA with rank-based mutation (CPOMSA-RM). A chaotic mapping technique is used in the proposed algorithm to initialize the moth’s population to enhance the global search ability. The proposed CPOMSA-RM is integrated with a chaotic local search scheme to fortify the local search skill. Furthermore, the number of prospector moths in the proposed algorithm is chaotically determined to achieve a better balance between diversification and intensification. The proposed algorithm is integrated with a rank oriented Levy flights based mutation technique to improve the intensification and the convergence speed. In addition, partial opposition-based learning is combined in the proposed algorithm to prevent sub-optimal solution entrapment. Here, diversification is the process of searching for new areas of the search space, while intensification is the process of refining the current solution. To confirm the proposed algorithm’s efficacy, it is compared with different algorithms using statistical measures and non-parametric statistical tests are performed on evolutionary computation based 2017 benchmark functions. Moreover, the proposed algorithm is applied to solve the optimal power flow problem while considering the optimal placement and configuration of a single flexible AC transmission system device, i.e. static synchronous series compensator, and the results are compared with existing sophisticated algorithms. The simulation results confirm that the proposed algorithm outperforms its competitors in terms of solution quality, accuracy, convergence speed, and statistical metrics for the optimal power flow problem and benchmark functions.</p>

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A novel variant of moth swarm algorithm for flexible AC transmission system-based optimal power flow problem

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Dhiman Banerjee,
  • Provas Kumar Roy,
  • Goutam Kumar Panda

摘要

This paper aims to develop a novel variant of the moth swarm algorithm (MSA) for addressing nonlinearity coupled and complex flexible AC transmission systems integrated optimal power flow problems for two standard IEEE test systems. The MSA is a swarm intelligence-based technique that imitates the moth’s navigational strategy towards moonlight. Despite its simple algorithmic structure and ability to explore a large search space for better solutions, the MSA has some drawbacks, including a sluggish convergence rate, insufficient intensification, and high chances of getting entrapped in the sub-optimal solution point. To address the limitations of the orthodox MSA, this paper devises a chaos-incorporated partial opposition-based MSA with rank-based mutation (CPOMSA-RM). A chaotic mapping technique is used in the proposed algorithm to initialize the moth’s population to enhance the global search ability. The proposed CPOMSA-RM is integrated with a chaotic local search scheme to fortify the local search skill. Furthermore, the number of prospector moths in the proposed algorithm is chaotically determined to achieve a better balance between diversification and intensification. The proposed algorithm is integrated with a rank oriented Levy flights based mutation technique to improve the intensification and the convergence speed. In addition, partial opposition-based learning is combined in the proposed algorithm to prevent sub-optimal solution entrapment. Here, diversification is the process of searching for new areas of the search space, while intensification is the process of refining the current solution. To confirm the proposed algorithm’s efficacy, it is compared with different algorithms using statistical measures and non-parametric statistical tests are performed on evolutionary computation based 2017 benchmark functions. Moreover, the proposed algorithm is applied to solve the optimal power flow problem while considering the optimal placement and configuration of a single flexible AC transmission system device, i.e. static synchronous series compensator, and the results are compared with existing sophisticated algorithms. The simulation results confirm that the proposed algorithm outperforms its competitors in terms of solution quality, accuracy, convergence speed, and statistical metrics for the optimal power flow problem and benchmark functions.