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Enhanced Sparrow Search Algorithm With Mutation Strategy for Global Optimization
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DOI：10.1109/ACCESS.2021.3129255.png)
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Abstract
En 中文
In order to improve the performance of the sparrow search algorithm (SSA), in this paper, a novel series of SSA variants is proposed by combining SSA with improved Tent chaos mutation (IT), Levy flights mutation (LF), elite opposition-based learning mutation (EOBL), variable radius mutation (VR) and the combination of IT, LF, EOBL, and VR, namely, ITSSA, LFSSA, EOBLSSA, VRSSA, and CMSSA, respectively. Initially, the performance of these variants is evaluated on a comprehensive set of 31 benchmark test functions. Moreover, the performance of the best algorithm among these variants is compared with 19 state-of-the-art optimization algorithms to validate its performance on 31 benchmark test functions. The convergence and computational complexity of the best variant are also analyzed to test exploration, exploitation, and local optima avoidance. It is then employed on eight real-world constrained engineering problems to further verify its robustness. The experimental results reveal that the best algorithm of SSA variants outperforms other competitors and is highly effective in solving real-life cases.
Keywords:
Optimization
Particle swarm optimization
Heuristic algorithms
Programming
Statistics
Genetic algorithms
Evolution (biology)
Sparrow search algorithm
improved Tent chaos mutation
Levy flights mutation
elite opposition-based learning mutation
variable radius mutation
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W