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首页> 外文期刊>International journal of electrical and power engineering >A Particle Swarm Optimization with Random Particles and Fine-Tuning Mechanism for Nonconvex Economic Dispatch
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A Particle Swarm Optimization with Random Particles and Fine-Tuning Mechanism for Nonconvex Economic Dispatch

机译：非凸经济调度的具有随机粒子和微调机制的粒子群算法

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摘要

This study presents a new approach to the economic dispatch problems with valve-point effects. The practical economic dispatch problem has a nonconvex cost function with equality and inequality constraints that it is difficult to find the optimal solutions using any mathematical approaches. A Particle Swarm Optimization (PSO) with Random Particles and Fine-tuning mechanism (PSO-RPFT) is proposed to solve economic dispatch problem. The proposed developed in such a way that PSO with Constriction Factor (PSO-CF) is applied as a based level search which can give a good direction to the optimal global region. Random particles and fine-tuning mechanism is used as a fine tuning to determine the optimal solutions at the final. Effectiveness of the proposed method is demonstrated on 3 example systems and compared to that of SA, GA, EP. Results show that the proposed method is more effective in solving economic dispatch problem.
机译：这项研究提出了一种具有阀点效应的经济调度问题的新方法。实际的经济调度问题具有具有等式和不等式约束的非凸成本函数，因此很难使用任何数学方法来找到最优解。为了解决经济调度问题，提出了一种具有随机粒子和微调机制的粒子群优化算法（PSO-RPFT）。提出的建议以具有收缩因子的PSO（PSO-CF）作为基础级别搜索的方式开发，可以为最佳全局区域提供良好的指导。随机粒子和微调机制被用作微调，以确定最终的最佳解。在3个示例系统上证明了该方法的有效性，并与SA，GA，EP的方法进行了比较。结果表明，该方法在解决经济调度问题上更为有效。

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