Cooperative metaheuristic algorithm for global optimization and engineering problems inspired by heterosis theory.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Swarm intelligence algorithms face challenges with local optima and slow convergence in large search spaces.
• Existing methods often struggle to balance global exploration and local exploitation effectively.

Purpose of the Study:

• To develop a novel cooperative metaheuristic algorithm (CMA) inspired by heterosis theory to address limitations in swarm intelligence.
• To enhance global optimization and engineering design problem-solving capabilities.

Main Methods:

• Developed a cooperative metaheuristic algorithm (CMA) simulating hybrid rice optimization (HRO) with three subpopulations.
• Implemented a three-phase local optima avoidance technique (Search-Escape-Synchronize - SES) within each subpopulation.
• Integrated Particle Swarm Optimization (PSO) for global search, Lévy flight for escape, and Ant Colony Optimization (ACO) for local exploitation.

Main Results:

• CMA demonstrated improved convergence rates and maintained population diversity.
• The algorithm effectively balanced global exploration and local exploitation.
• CMA outperformed 10 state-of-the-art algorithms on 26 benchmark functions and 5 engineering problems.

Conclusions:

• The proposed cooperative metaheuristic algorithm (CMA) is effective for global optimization and engineering design.
• CMA offers a promising approach to overcoming the limitations of traditional swarm intelligence algorithms.
• The heterosis-inspired cooperative strategy and SES technique significantly enhance optimization performance.