A Labor Division Artificial Gorilla Troops Algorithm for Engineering Optimization.

Area of Science:

• Optimization Algorithms
• Computational Intelligence
• Metaheuristics

Background:

• Conventional Artificial Gorilla Troops Optimizer (GTO) uses uniform search equations, limiting performance.
• Lack of labor division in GTO restricts adaptive exploration and exploitation.
• Uniform approach hinders optimization efficiency in complex problems.

Purpose of the Study:

• To introduce an enhanced Labor Division Gorilla Troops Optimizer (LDGTO).
• To address the limitations of uniform search strategies in GTO.
• To improve the performance of metaheuristic algorithms through labor division.

Main Methods:

• Incorporated natural mechanisms of labor division and outcome allocation.
• Designed a stimulus-response model for adaptive task differentiation (exploration/exploitation).
• Implemented three behavioral development modes (self-enhancement, competence maintenance, elimination) for different individual stages.

Main Results:

• LDGTO demonstrated superior performance across benchmark test suites (unimodal, multimodal, combinatorial functions).
• Evaluated on real-world engineering applications: four-bar transplanter mechanism design and color image segmentation.
• Consistently outperformed three GTO variants and seven state-of-the-art metaheuristic algorithms.

Conclusions:

• LDGTO effectively improves optimization performance through labor division and adaptive strategies.
• The proposed model offers a more efficient and robust metaheuristic approach.
• LDGTO shows significant potential for solving complex optimization problems in various domains.