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

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Abstract

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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Authors and Affiliations

  1. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, 85281, USA

    Leren Qian

  2. Department of Marine Electronics and Communication Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran

    Mohammad Khishe

  3. Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA

    Yiqian Huang

  4. Centre for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia

    Seyedali Mirjalili

  5. University Research and Innovation Center, Obuda University, Budapest, 1034, Hungary

    Seyedali Mirjalili

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  1. Leren Qian
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Qian, L., Khishe, M., Huang, Y. et al. SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behavior. Neural Comput & Applic 36, 4763–4786 (2024). https://doi.org/10.1007/s00521-023-09236-y

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