SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behavior

The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees’ individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the...

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Vydáno v:Neural computing & applications Ročník 36; číslo 9; s. 4763 - 4786
Hlavní autoři: Qian, Leren, Khishe, Mohammad, Huang, Yiqian, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.03.2024
Springer Nature B.V
Témata:
Artificial Intelligence
Benchmarks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Gas solubility
Genetic algorithms
Hunting
Image Processing and Computer Vision
Iterative algorithms
Iterative methods
Optimization algorithms
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Chimp optimization algorithm
Spiral model
Optimization
Metaheuristic algorithms
ISSN:0941-0643, 1433-3058
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Shrnutí:The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees’ individual intelligence and hunting behaviors. In this algorithm, the hunting process consists of four steps: driving, blocking, chasing, and attacking. Because of the novelty of ChOA, the steps of the hunting process have been modeled in the simplest possible way, leading to slow and premature convergence similar to other iterative algorithms. This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to rectify the abovementioned deficiencies. The SEB-ChOAs’ performance is evaluated on 23 standard benchmarks, 20 benchmarks of IEEE CEC-2005, 10 cases of IEEE CEC06-2019 test-suite, and 12 constrained real-world engineering problems of IEEE CEC-2020. The SEB-ChOAs are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the most well-known optimization algorithms, Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), Henry Gas Solubility Optimization (HGSO), as almost novel optimization algorithms, and jDE100 and DISHchain1e+12, as winners of IEEE CEC06-2019 competition, and also EBOwithCMAR and CIPDE as superior secondary optimization algorithms. The SEB-ChOAs reached the first rank among almost all benchmarks and demonstrated very competitive results compared to jDE100 and DISHchain1e+12 as the best-performing optimizers. Statistical evidence shows that the SEB-ChOA outperforms the PSO, GA, SMA, MPA, ALO, and HGSO optimizers while producing results comparable to those of the jDE100 and DISHchain1e+12 algorithms.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09236-y