Overcoming center-bias behavior: A metaheuristic algorithm with dual operators for optimized search and refinement
Erik Cuevas, Oscar M. Gonzalez, Héctor Escobar, Ernesto Ayala, Daniel Zaldívar, Marco Pérez‐Cisneros, Alma N. Rodríguez-Vázquez · Systems and Soft Computing · 2025
In most metaheuristic methods, a single operator is employed for both exploration and exploitation. While this makes the algorithm simple, this approach can introduce inefficiencies, such as inadequate coverage of areas, revisiting irrelevant spaces, and poor refinement of solutions. In this paper, a new metaheuristic algorithm using two operators designed specifically to perform exploration and exploitation tasks is introduced. During the initial phase, the exploration operator constructs a trajectory formed by the sequential interpolation of points obtained using the Latin Hypercube Sampling technique. The trajectory completely covers the areas in the search space and acts as a guide mechanism for exploring the runtime of the algorithm. As the search continues, each agent changes its position to follow this track such that the particles continue to explore the search domain and identify its most promising regions. In contrast, the exploitation operator uses a crossover operation in which an agent’s present position is modified to a new position inside a ribbon-shaped area using its position combined with the best solutions found until then, allowing the exploitation phase to concentrate on refining these promising solutions further. Together, these operators provide a balanced approach to exploring and exploiting the search space, enhancing the algorithm’s overall effectiveness. The efficacy of the proposed approach was validated by comparing the algorithm to various metaheuristic algorithms using a standard set of functions that have been shifted to accurately assess the performance of methods employing center-biased operators. The findings indicate that this method yields competitive outcomes, providing superior quality solutions and quicker convergence rates, while avoiding the drawbacks associated with algorithms reliant on center-biased operators.