Journal Article10.1016/j.asoc.2022.108798
Multi/many-objective evolutionary algorithm assisted by radial basis function models for expensive optimization
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TL;DR: In this paper , a multi-many-objective optimization algorithm assisted by radial basis function is proposed based on reference vectors to solve computationally expensive optimization, where a set of candidates are first determined by the reference vectors guided evolutionary algorithm in a sub-cycle.
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About: This article is published in Applied Soft Computing. The article was published on 01 Apr 2022. The article focuses on the topics: Computer science & Benchmark (surveying).
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Surrogate-assisted evolutionary algorithm with decomposition-based local learning for high-dimensional multi-objective optimization
Jian-Xian Shen,Peng Wang,Huachao Dong,Wenxin Wang,Jinglu Li +4 more
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References
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TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Comparison of multiobjective evolutionary algorithms: empirical results
Eckart Zitzler,Kalyanmoy Deb,Lothar Thiele +2 more
- 01 Jan 1999
TL;DR: In this article, the authors provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions, each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front.
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Multiquadric equations of topography and other irregular surfaces
TL;DR: In this paper, a method of representing irregular surfaces that involves the summation of equations of quadric surfaces having unknown coefficients is described, and procedures are given for solving multiquadric equations of topography that are based on coordinate data.
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