SEB-ChOA: An Improved Chimp Optimization Algorithm Using Spiral Exploitation Behavior
Leren Qian, Mohammad Khishe, Yiqian Huang, Seyedali Mirjalili

TL;DR
This paper introduces SEB-ChOA, an enhanced chimp optimization algorithm with spiral exploitation behavior, demonstrating superior performance on various benchmarks and real-world problems compared to several existing algorithms.
Contribution
The paper proposes six spiral functions and two hybrid spiral functions to improve ChOA's convergence speed and accuracy, addressing its previous limitations.
Findings
SEB-ChOA outperforms traditional algorithms on most benchmarks.
The hybrid spiral functions enhance convergence and solution quality.
SEB-ChOA achieves competitive results with state-of-the-art optimizers.
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Advanced Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms
