SEB-ChOA: An Improved Chimp Optimization Algorithm Using Spiral Exploitation Behavior

• URL: http://arxiv.org/abs/2512.05981v1
• Date: Wed, 26 Nov 2025 23:46:29 GMT
• Title: SEB-ChOA: An Improved Chimp Optimization Algorithm Using Spiral Exploitation Behavior
• Authors: Leren Qian, Mohammad Khishe, Yiqian Huang, Seyedali Mirjalili,
• Abstract summary: The chimp optimization algorithm (ChOA) is a nature-inspired algorithm that imitates chimpanzees' individual intelligence and hunting behaviors.<n>This paper proposes six spiral functions and introduces two novel hybrid spiral functions (SEB-ChOA) to address these deficiencies.<n>The performance of SEB-ChOA is evaluated on 23 standard benchmarks, 20 benchmarks of the IEEE CEC-2005 test suite, 10 cases from the IEEE CEC06-2019 test suite, and 12 constrained real-world engineering problems.
• Score: 22.87976383190481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 a simple 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 address these deficiencies. The performance of SEB-ChOA is evaluated on 23 standard benchmarks, 20 benchmarks of the IEEE CEC-2005 test suite, 10 cases from the IEEE CEC06-2019 test suite, and 12 constrained real-world engineering problems from IEEE CEC-2020. The SEB-ChOA variants are compared with three groups of optimization algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as well-known optimizers; Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Ant Lion Optimization (ALO), and Henry Gas Solubility Optimization (HGSO) as recently developed optimizers; and jDE100 and DISHchain1e+12, the winners of the IEEE CEC06-2019 competition. Additional comparisons are made with EBOwithCMAR and CIPDE as strong secondary baselines. The SEB-ChOA methods achieve top-ranked results on nearly all benchmarks and show competitive performance compared to jDE100 and DISHchain1e+12. Statistical results indicate that SEB-ChOA outperforms PSO, GA, SMA, MPA, ALO, and HGSO while producing results comparable to those of jDE100 and DISHchain1e+12.

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