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

2023-12-19

Neural computing & applications (Print)

AI Summary

To address the slow convergence and premature convergence issues of the original Chimp Optimization Algorithm (ChOA), this paper proposes a Spiral-Enhanced ChOA (SE-ChOA). It introduces a novel Spiral Exploitation Behavior (SEB) modeling mechanism, incorporating six spiral functions and two new hybrid spiral update strategies to dynamically balance global exploration and local exploitation. Additionally, multi-stage population diversity preservation and adaptive search path design significantly improve convergence accuracy and robustness. Comprehensive evaluations on 23 classical, 53 CEC2010–2022 benchmark functions, and 12 engineering constrained optimization problems demonstrate that SE-ChOA consistently outperforms mainstream algorithms—including PSO and GA—and achieves performance comparable to the CEC2019 champion algorithms jDE100 and DISHchain1e+12. These results validate SE-ChOA’s superiority, effectiveness, and practical applicability in complex optimization tasks.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Addresses slow and premature convergence in the Chimp Optimization Algorithm.
• Introduces spiral functions to improve hunting process modeling in ChOA.
• Evaluates performance on benchmarks and real-world engineering problems.

Innovation

• Introduces six spiral functions to improve algorithm convergence
• Proposes two novel hybrid spiral functions for enhanced exploitation
• Achieves top-ranked results across multiple benchmark test suites