Evolutionary Computation and Multi-objective Optimization

Dear Colleagues,

Evolutionary computation is an area of artificial intelligence that uses algorithms inspired by natural evolution to solve optimization problems. This area’s success and rapid growth are largely due to its effectiveness at solving complex problems, aligned with emerging real-world issues for which classical optimization techniques do not provide satisfactory results. Moreover, evolutionary algorithms are conceptualized relatively simply and have an affordable computational cost.

Progress in modern society involves increasingly complex problems represented by more sophisticated optimization models. In this sense, it is possible to find problems that model diverse situations and consider multiple optimization criteria; include decision variables in continuous, discrete, or mixed spaces; impose constraints on possible solutions; consider changes in criteria and limitations over time; or different decision levels. In particular, multi-objective optimization problems are interesting because of their ability to model situations in which two or more conflicting criteria need to be satisfied, which occur quite frequently in the real world.

For multi-objective optimization problems, evolutionary algorithms search for a set of trade-off solutions, allowing decision-makers to understand each solution’s implications better and make more informed choices. This approach is essential in fields such as engineering, economics, and logistics, where balancing different criteria is crucial for effective problem-solving.

Therefore, this Special Issue will disseminate recent advances in evolutionary computation with a particular emphasis on the treatment of multi-objective optimization problems, including, but not limited to, publications presenting new designs of algorithms, operators, metrics, etc., or clear improvements in state-of-the-art existing examples; innovative applications of evolutionary algorithms in different areas of this field; and theoretical and practical studies that broaden the landscape of this area.

Dr. Alejandro Rodríguez-Molina
Prof. Dr. Miguel Gabriel Villarreal-Cervantes
Dr. Efrén Mezura-Montes
Guest Editors

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• evolutionary algorithms
• multi-objective bi-level optimization
• dynamic and robust multi-objective optimization
• multi-party multi-objective optimization
• multi-objective multi-tasking optimization
• hybrid optimization techniques
• real-world applications
• multi-objective optimization
• large-scale multi-objective optimization
• multi-modal multi-objective optimization