Greedy Man Optimization Algorithm

Greedy Man Optimization Algorithm (GMOA) is a nature-inspired metaheuristic optimization algorithm developed by Hamed Nozari in 2024. The algorithm is inspired by the behavioral interaction between a "greedy man" and "resource parasites," simulating the competitive nature of resource consumption and resistance. It is designed to efficiently balance exploration and exploitation in solving complex optimization problems.

Inspiration and Mechanism

The core concept of GMOA revolves around modeling a greedy individual's attempt to monopolize resources while being confronted by parasites that develop resistance. This metaphor is implemented mathematically to create a dynamic balance between intensification and diversification. GMOA introduces competitive operators, resistance adaptation, and resource allocation dynamics to improve convergence.

Mathematical Model

The algorithm defines greedy agents and parasite populations with update rules for position, resistance, and dominance. The population evolves iteratively using stochastic and deterministic components inspired by natural competition. The mathematical framework mimics:

• Resource greediness (exploration pressure)
• Parasite resistance (diversification)
• Stability through adaptive penalty functions

Applications

GMOA has been applied in various optimization contexts, including engineering design, feature selection, scheduling, and multi-objective problems. Its competitive structure makes it suitable for problems with rugged landscapes and deceptive local optima.

Implementation

A MATLAB implementation of GMOA is publicly available on MATLAB Central File Exchange.

References

• Nozari, Hamed (2025). "Greedy Man Optimization Algorithm: A Novel Approach to Problem Solving with Resistant Parasites". Journal of Industrial and Systems Engineering.
• "Greedy Man Optimization Algorithm (GMOA)". ResearchGate. Retrieved 31 March 2025.
• "Greedy Man Optimization Algorithm (GMOA)". Academia.edu. Retrieved 31 March 2025.

See also

• Metaheuristic
• Swarm intelligence
• Evolutionary algorithm

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