Self-adaptive Bat Algorithm With Genetic Operations

Jing Bi, Haitao Yuan, Jiahui Zhai, Meng Chu Zhou, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA's efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.

Original languageEnglish (US)
Pages (from-to)1284-1294
Number of pages11
JournalIEEE/CAA Journal of Automatica Sinica
Volume9
Issue number7
DOIs
StatePublished - Jul 1 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Control and Optimization
  • Artificial Intelligence

Keywords

  • Bat algorithm (BA)
  • Genetic algorithm (GA)
  • Hybrid algorithm
  • Learning mechanism
  • Meta-heuristic optimization algorithms

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