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Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects

机译：基于量子力学和谐波振子势能的粒子群方法在具有阀点效应的经济负荷分配中

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摘要

Particle swarm optimization (PSO) algorithm is population-based heuristic global search algorithm inspired by social behavior patterns of organisms that live and interact within large groups. The PSO is based on researches on swarms such as fish schooling and bird flocking. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-inspired version of the PSO (QPSO) using the harmonic oscillator potential well (HQPSO) to solve economic dispatch problems. A 13-units test system with incremental fuel cost function that takes into account the valve-point loading effects is used to illustrate the effectiveness of the proposed HQPSO method compared with the simulation results based on the classical PSO, the QPSO, and other optimization algorithms reported in the literature.
机译：粒子群优化（PSO）算法是一种基于种群的启发式全局搜索算法，其灵感来自在大型群体中生活和互动的生物的社会行为模式。 PSO是基于对群体的研究，例如鱼类养殖和鸟类聚集。受经典PSO方法和量子力学理论的启发，这项工作提出了一种使用PSO（QPSO）的量子启发版本，它使用谐波振荡器势阱（HQPSO）解决了经济调度问题。使用一个具有13个单元的测试系统，该系统具有考虑了阀点负载影响的增量燃料成本函数，与基于经典PSO，QPSO和其他优化算法的仿真结果相比，该示例系统说明了HQPSO方法的有效性文献报道。

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