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Engineering with Computers
Published in:

11-04-2022 | Original Article

Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems

Authors: Amir Seyyedabbasi, Farzad Kiani

Published in: Engineering with Computers | Issue 4/2023

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Abstract

This study proposes a new metaheuristic algorithm called sand cat swarm optimization (SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are able to detect low frequencies below 2 kHz and also have an incredible ability to dig for prey. The proposed algorithm, inspired by these two features, consists of two main phases (search and attack). This algorithm controls the transitions in the exploration and exploitation phases in a balanced manner and performed well in finding good solutions with fewer parameters and operations. It is carried out by finding the direction and speed of the appropriate movements with the defined adaptive strategy. The SCSO algorithm is tested with 20 well-known along with modern 10 complex test functions of CEC2019 benchmark functions and the obtained results are also compared with famous metaheuristic algorithms. According to the results, the algorithm that found the best solution in 63.3% of the test functions is SCSO. Moreover, the SCSO algorithm is applied to seven challenging engineering design problems such as welded beam design, tension/compression spring design, pressure vessel design, piston lever, speed reducer design, three-bar truss design, and cantilever beam design. The obtained results show that the SCSO performs successfully on convergence rate and in locating all or most of the local/global optima and outperforms other compared methods.
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Appendix
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Metadata
Title
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems
Authors
Amir Seyyedabbasi
Farzad Kiani
Publication date
11-04-2022
Publisher
Springer London
Published in
Engineering with Computers / Issue 4/2023
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-022-01604-x

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