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An Enhanced Lightning Attachment Procedure Optimization Algorithm

School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
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Algorithms 2019, 12(7), 134; https://doi.org/10.3390/a12070134
Received: 23 May 2019 / Revised: 27 June 2019 / Accepted: 28 June 2019 / Published: 29 June 2019
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Abstract

To overcome the shortcomings of the lightning attachment procedure optimization (LAPO) algorithm, such as premature convergence and slow convergence speed, an enhanced lightning attachment procedure optimization (ELAPO) algorithm was proposed in this paper. In the downward leader movement, the idea of differential evolution was introduced to speed up population convergence; in the upward leader movement, by superimposing vectors pointing to the average individual, the individual updating mode was modified to change the direction of individual evolution, avoid falling into local optimum, and carry out a more fine local information search; in the performance enhancement stage, opposition-based learning (OBL) was used to replace the worst individuals, improve the convergence rate of population, and increase the global exploration capability. Finally, 16 typical benchmark functions in CEC2005 are used to carry out simulation experiments with LAPO algorithm, four improved algorithms, and ELAPO. Experimental results showed that ELAPO obtained the better convergence velocity and optimization accuracy. View Full-Text
Keywords: lightning attachment procedure optimization algorithm; differential evolution; opposition-based learning; meta-heuristic optimization lightning attachment procedure optimization algorithm; differential evolution; opposition-based learning; meta-heuristic optimization
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Wang, Y.; Jiang, X. An Enhanced Lightning Attachment Procedure Optimization Algorithm. Algorithms 2019, 12, 134.

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