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<p>The economic load dispatch problem owns features like multimodality, non-convexity, and hence, the traditional programming models fail to offer a solution to it. We have shown our research interest in PSO to solve the problem due to the umpteen numbers of applications it supports along with their improved swarming intelligence. Our previous paper has been dealt with an improved form of PSO, wherein adaptive acceleration constants were exploited. The adaptive nature comes from the dynamic changing of the acceleration constants with regard to the particle’s position as well as the number of function assessments. In that algorithm, the particles were enabled to search in a systematic distributed environment and the algorithm was known as PSO with Distributed Acceleration Constant (PSODAC). This paper makes an extension of PSODAC through hybridizing it with the notion behind Differential Evolution (DE). The hybridization has been performed in a sequential manner. Hence, it achieves the name as Sequentially Hybridized DE and PSODAC (SH-DEPSODAC), irrespective of the fact that the PSODAC has been employed. A total of three test systems have been utilized to test and examine the superiority of PSODAC for exhibiting higher particle dynamics.</p>

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Hybridization of differential evolution and particle swarm optimization with distributed acceleration constants to solve economic load dispatch problem

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Naresh Kumar Yadav

摘要

The economic load dispatch problem owns features like multimodality, non-convexity, and hence, the traditional programming models fail to offer a solution to it. We have shown our research interest in PSO to solve the problem due to the umpteen numbers of applications it supports along with their improved swarming intelligence. Our previous paper has been dealt with an improved form of PSO, wherein adaptive acceleration constants were exploited. The adaptive nature comes from the dynamic changing of the acceleration constants with regard to the particle’s position as well as the number of function assessments. In that algorithm, the particles were enabled to search in a systematic distributed environment and the algorithm was known as PSO with Distributed Acceleration Constant (PSODAC). This paper makes an extension of PSODAC through hybridizing it with the notion behind Differential Evolution (DE). The hybridization has been performed in a sequential manner. Hence, it achieves the name as Sequentially Hybridized DE and PSODAC (SH-DEPSODAC), irrespective of the fact that the PSODAC has been employed. A total of three test systems have been utilized to test and examine the superiority of PSODAC for exhibiting higher particle dynamics.