Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm

Jeng Shyang Pan, Jie Shan, Shi Guang Zheng, Shu Chuan Chu, Cheng Kuo Chang

Research output: Contribution to journalArticlepeer-review

Abstract

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.

Original languageEnglish
Number of pages16
JournalCluster Computing
DOIs
Publication statusE-pub ahead of print - 12 Feb 2021

Keywords

  • Communication strategy
  • Neural network
  • Parallel salp swarm algorithm
  • Prediction of wind power
  • Swarm intelligence optimization

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