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DSPSO-TSA for economic dispatch problem with nonsmooth and noncontinuous cost functions

机译：具有非平稳和非连续成本函数的经济调度问题的DSPSO-TSA

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摘要

This paper proposes a new approach based on particle swarm optimization (PSO) and tabu search algorithm (TSA). This proposed approach is called distributed Sobol PSO and TSA (DSPSO-TSA). In order to improve the convergence characteristic and solution quality of searching process, three mechanisms had been presented. Firstly, the Sobol sequence is applied to generate an inertia factor instead of the existing process. Secondly, a distributed process is used so as to reach the global solution rapidly.rnThe search process is divided to multi-stages and used a short-term memory for recognition the best search history. Finally, to guarantee the global solution, TSA had been activated to adjust the obtained solution of DSPSO algorithm. To show its effectiveness, the proposed DSPSO-TSA is applied to test four case studies of economic dispatch (ED) problem considering nonsmooth and noncontinuous fuel cost functions of generating units. The simulation results obtained from DSPSO-TSA are compared with conventional approaches such as genetic algorithm (GA), TSA, PSO, and others in literatures. The comparison results show that the efficiency of proposed approach can reach higher quality solution and faster computational time than the conventional methods.
机译：本文提出了一种基于粒子群优化（PSO）和禁忌搜索算法（TSA）的新方法。此提议的方法称为分布式Sobol PSO和TSA（DSPSO-TSA）。为了提高搜索过程的收敛性和求解质量，提出了三种机制。首先，使用Sobol序列代替现有过程来生成惯性因子。其次，使用分布式过程以快速地找到全局解决方案。搜索过程分为多个阶段，并使用短期记忆来识别最佳搜索历史。最后，为保证全局解决方案，已激活TSA来调整所获得的DSPSO算法的解决方案。为了显示其有效性，将拟议的DSPSO-TSA应用于测试考虑了发电机组非平稳和非连续燃料成本函数的经济调度（ED）问题的四个案例研究。从DSPSO-TSA获得的仿真结果与传统方法（如遗传算法（GA），TSA，PSO等）进行了比较。比较结果表明，与传统方法相比，所提方法的效率更高，解决方案质量更高，计算时间更快。

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