Greedy Man Optimization Algorithm (GMOA)

The Greedy Man Optimization Algorithm (GMOA), developed by Hamed Nozari, is a novel bio-inspired metaheuristic designed to solve complex single and multi-objective optimization problems. GMOA draws inspiration from human behavior, specifically the metaphor of individuals who tenaciously hold on to their positions, modeled as resistant solutions influenced by metaphorical parasites (MMOs). The algorithm introduces two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, ensuring stability and diversity, and periodic parasite removal, which promotes mutation and prevents stagnation. By balancing exploration and exploitation dynamically, GMOA effectively navigates complex, multimodal search spaces, making it highly robust against local optima. This algorithm has been successfully applied to various benchmark functions and can handle constrained and unconstrained optimization problems. GMOA’s ability to maintain a Pareto archive also makes it suitable for multi-objective optimization, offering a promising new approach in the field of evolutionary computation.