Browsing by Keyword "Evolutionary multitasking"
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Item COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking: A coevolutionary bat algorithm for discrete evolutionary multitasking(Springer Nature, 2020) Osaba, Eneko; Del Ser, Javier; Yang, Xin-She; Iglesias, Andres; Galvez, Akemi; Krzhizhanovskaya, Valeria V.; Závodszky, Gábor; Lees, Michael H.; Sloot, Peter M.A.; Sloot, Peter M.A.; Sloot, Peter M.A.; Dongarra, Jack J.; Brissos, Sérgio; Teixeira, João; Quantum; IAMultitasking 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.Item Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions: a Methodological Overview, Challenges, and Future Research Directions(2022-04-12) Osaba, Eneko; Del Ser, Javier; Martinez, Aritz D.; Hussain, Amir; Quantum; IAIn this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of evolutionary multitasking tackles multitask optimization scenarios by using biologically inspired concepts drawn from swarm intelligence and evolutionary computation. The main purpose of this survey is to collect, organize, and critically examine the abundant literature published so far in evolutionary multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can leverage the potential of biologically inspired algorithms for multitask optimization. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.