Abstract
Many real-world optimization problems truly involve multiple objectives and, therefore, are better posed as multi-objective optimization problems. Interestingly, most multi-objective optimization problems give rise to a set of optimal solutions, called Pareto-optimal solutions, instead of a single optimal solution. The optimal solutions are different from each other, primarily varying in the relative importance of each objective. In the absence of any priori information of different objectives, it becomes important to find as many such Pareto-optimal solutions as possible. Since classical optimization methods are not efficient in finding multiple optimal solutions and evolutionary algorithms are ideal for finding multiple optimal solutions, it is natural that there has been a considerable interest among evolutionary algorithmists to concentrate on solving these problems. In this paper, we present a brief overview of the past research activities, discuss current salient methodologies, and highlight some immediate future research in this area.
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Deb, K. (2000). Multi-Objective Evolutionary Optimization: Past, Present, and Future. In: Parmee, I.C. (eds) Evolutionary Design and Manufacture. Springer, London. https://doi.org/10.1007/978-1-4471-0519-0_18
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DOI: https://doi.org/10.1007/978-1-4471-0519-0_18
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