Hybrid multi-strategy chaos somersault foraging chimp optimization algorithm research.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Chimp Optimization Algorithm (ChOA) suffers from slow convergence and susceptibility to local optima.
• Existing intelligent optimization algorithms often struggle with maintaining diversity and achieving high accuracy.
• Addressing these limitations is crucial for effective problem-solving in complex computational tasks.

Purpose of the Study:

• To propose a Chaos Somersault Foraging Chimp Optimization Algorithm (CSFChOA) to overcome ChOA's limitations.
• To enhance convergence speed, improve accuracy, and prevent premature convergence to local optima.
• To validate the algorithm's performance on standard test functions and engineering design problems.

Main Methods:

• Introduction of a cat chaotic sequence for generating diverse initial solutions.
• Application of opposition-based learning to select superior initial populations.
• Implementation of a somersault foraging strategy using the optimal solution as a pivot to increase population diversity and search scope.

Main Results:

• CSFChOA demonstrated superior performance over ChOA and other algorithms on 23 standard and CEC2019 test functions.
• Statistical analysis using the Wilcoxon rank sum test confirmed CSFChOA's robustness and convergence accuracy.
• Significant improvements were observed in engineering design problems, including a 100% cost reduction for speed reducers and 6.77% for three-bar trusses.

Conclusions:

• The proposed CSFChOA effectively addresses the convergence speed and accuracy issues of the original ChOA.
• The integration of chaotic sequences, opposition-based learning, and somersault foraging enhances global optimization capabilities.
• CSFChOA exhibits strong feasibility, applicability, and superiority in solving complex engineering design problems.