An efficient multiobjective differential evolution algorithm for engineering design |
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Authors: | Wenyin Gong Zhihua Cai Li Zhu |
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Affiliation: | (1) School of Computer Science, China University of Geosciences, Wuhan, 430074, China |
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Abstract: | Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much
attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented
for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate
the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated
solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new
hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints,
a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The
proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling
mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate
the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting
Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be
statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions
with good coverage and convergence to true Pareto-optimal fronts. |
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Keywords: | Engineering design Multiobjective optimization Differential evolution algorithm Orthogonal design method Pareto-adaptive ϵ -dominance Constraint-handling method |
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