Abstract
Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Many years of research and application studies have produced a number of efficient multi-objective evolutionary algorithms (MOEAs), which are ready to be applied to real-world problems. In this paper, we propose a practical approach, which will enable an user to find a set of non-dominated solutions closer to the true Pareto-optimal front and simultaneously reduce the size of the obtained non-dominated solution set. The efficacy of the proposed approach is demonstrated in solving a number of mechanical shape optimization problems, including a simply-supported plate design, a cantilever plate design, a hoister design, and a bicycle frame design. The results are interesting and suggest immediate application of the prop osed technique to more complex engineering design problems.
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Deb, K., Goel, T. (2003). Multi-Objective Evolutionary Algorithms for Engineering Shape Design. In: Evolutionary Optimization. International Series in Operations Research & Management Science, vol 48. Springer, Boston, MA. https://doi.org/10.1007/0-306-48041-7_6
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DOI: https://doi.org/10.1007/0-306-48041-7_6
Publisher Name: Springer, Boston, MA
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