COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking
This work addresses multitasking optimization for researchers in evolutionary computation, but it is incremental as it combines existing co-evolutionary strategies and the Bat Algorithm.
The authors tackled the problem of solving multiple optimization tasks simultaneously by proposing a Coevolutionary Bat Algorithm (COEBA) for discrete evolutionary multitasking, and demonstrated its promising performance by comparing it with the Multifactorial Evolutionary Algorithm across 15 multitasking setups based on Traveling Salesman Problem instances.
Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.