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Portia spider algorithm: an evolutionary computation approach for engineering application
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DOI：10.1007/s10462-023-10683-1.png)
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Abstract
En 中文
The Portia spider, a notable member of the jumping spider family (Salticidae), is widely recognized for its intricate hunting strategies and remarkable problem-solving prowess. Several species fall under the Portia genus, with habitats spanning regions in Africa, Asia, and Australia. Demonstrating the ability to tackle new challenges, these spiders can learn and adapt their strategies based on prior experiences. This study introduces the Portia Spider Algorithm (PSA), a swarm-based technique inspired by the unique predatory strategies of the Portia spider. We conducted rigorous assessments of PSA performance against 23 classical test functions, 29 CEC2017 test cases, and 5 engineering optimization tasks. To demonstrate the effectiveness of the PSA, outcomes were juxtaposed with those of renowned algorithms. This paper explores the mechanics, advantages, and potential applications of PSA within the vast domain of computational optimization.
Keywords:
Swarm-based algorithm
Engineering optimization
Hunting behavior
Evolutionary algorithm
Benchmark
Particle swarm optimization
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