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Multitasking optimization is an emerging research field which has attractedlot of attention in the scientific community. The main purpose of this paradigmis how to solve multiple optimization problems or tasks simultaneously byconducting a single search process. The main catalyst for reaching thisobjective is to exploit possible synergies and complementarities among thetasks to be optimized, helping each other by virtue of the transfer ofknowledge among them (thereby being referred to as Transfer Optimization). Inthis context, Evolutionary Multitasking addresses Transfer Optimizationproblems by resorting to concepts from Evolutionary Computation forsimultaneous solving the tasks at hand. This work contributes to this trend byproposing a novel algorithmic scheme for dealing with multitaskingenvironments. The proposed approach, coined as Coevolutionary Bat Algorithm,finds its inspiration in concepts from both co-evolutionary strategies and themetaheuristic Bat Algorithm. We compare the performance of our proposed methodwith that of its Multifactorial Evolutionary Algorithm counterpart over 15different multitasking setups, composed by eight reference instances of thediscrete Traveling Salesman Problem. The experimentation and results stemmingtherefrom support the main hypothesis of this study: the proposedCoevolutionary Bat Algorithm is a promising meta-heuristic for solvingEvolutionary Multitasking scenarios.

COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking