Forgetting velocity based improved comprehensive learning particle swarm optimization for non-convex economic dispatch problems with valve-point effects and multi-fuel options

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Mathematics & Actuarial Science Department

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Shengping Xu, Guojiang Xiong, Ali Wagdy Mohamed, Houssem R.E.H. Bouchekara

Document Type

Research Article

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Publication Date

Fall 10-1-2022




Economic dispatch (ED) plays an essential role in the operation and planning of power systems. Mathematically, it turns to be a multi-constraint, multimodal, non-linear, and multivariate coupling optimization problem when considering the valve-point effects and multi-fuel options. In this paper, an improved comprehensive learning particle swarm optimization (CLPSO) named FV-ICLPSO is presented to solve it. FV-ICLPSO introduces three improved components to conquer the issue of slow convergence rate of CLPSO: (1) an adaptive strategy by using both the iteration and problem's dimensionality is presented to tune the learning probability; (2) an adaptive method is designed to give different particles in different levels different scopes to pick the source particles to construct their learning exemplars; and (3) an improved velocity updating formula based on forgetting the previous velocity is proposed to guide particles to fly towards more promising areas. FV-ICLPSO is first validated on thirty CEC2014 benchmark numerical functions and then applied to eight ED cases with different units from 10-Unit to 640-Unit. Simulation results demonstrate the strong competitiveness of FV-ICLPSO compared with CLPSO, other state-of-the-art algorithms, and the reported results of some recently published ED solution methods. Furthermore, the effect of the three improved components on FV-ICLPSO is also investigated.

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