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

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.