A chimp algorithm based on the foraging strategy of manta rays and its application.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Chimp Optimization Algorithm (ChOA) suffers from performance limitations, particularly a tendency towards local optima.
• Enhancing population diversity and balancing exploration/exploitation are critical for improving metaheuristic algorithm efficiency.

Purpose of the Study:

• To develop an improved optimization algorithm, the Manta Ray-based Chimpa Optimization Algorithm (MChOA), to overcome ChOA's limitations.
• To enhance the initial population distribution and convergence behavior for better optimization performance.

Main Methods:

• Incorporated the Latin hypercube method for a more diverse initial population distribution.
• Introduced nonlinear convergence factors based on positive cut functions to balance early exploration and later exploitation.
• Integrated the manta ray foraging strategy to mitigate local optimization issues.

Main Results:

• Experimental results on 27 benchmark functions demonstrate MChOA's significant performance advantages over other algorithms.
• The algorithm effectively improved optimization performance by addressing local optima.
• MChOA showed superior performance in solving complex scheduling problems in flexible workshops and aviation engine job shops.

Conclusions:

• The proposed Manta Ray-based Chimpa Optimization Algorithm (MChOA) offers a robust and effective solution for complex optimization tasks.
• MChOA demonstrates strong applicability and efficacy in real-world industrial scheduling problems, confirming its practical value.