Journal Article10.1016/j.ins.2023.119308
Adaptive multi-surrogate and module-based optimization algorithm for high-dimensional and computationally expensive problems
Meng-Ting Wu,Jin Xu,Lingling Wang,Chengxiao Zhang,Hongwu Tang +4 more
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TL;DR: In this paper , an adaptive multi-surrogate and module-based optimization algorithm named AMSMO is proposed to solve high-dimensional optimization problems, which makes use of five modules to promote the optimization quality.
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About: This article is published in Information Sciences. The article was published on 01 Oct 2023. The article focuses on the topics: Benchmark (surveying) & Computer science.
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Citations
Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey
Mengchu Zhou,Meiji Cui,Dian Xu,Shuwei Zhu,Ziyan Zhao,Abdullah Abusorrah +5 more
TL;DR: A comprehensive survey of surrogate-assisted evolutionary algorithms for HEPs from four main aspects and indicates open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.
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Dynamic Niching Particle Swarm Optimization with an External Archive-Guided Mechanism for Multimodal Multi-objective Optimization
Yu Sun,Yuqing Chang,Shengxiang Yang,Fuli Wang +3 more
TL;DR: This paper proposes DNPSO-AG, a dynamic niching particle swarm optimization algorithm with an external archive-guided mechanism, to solve multimodal multi-objective optimization problems, outperforming seven state-of-the-art competitors on the CEC 2019 test suite with 21.3% and 9.1% improvements.
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Multi-phase ensemble-RBF-assisted differential evolution for high-dimensional expensive optimization
Majid Ilchi Ghazaan,Parnian Mirghazanfari +1 more
Random Matrix-Based Genetic Algorithm: An Efficient Yet Privacy-Preserving Optimization Method
Bing Sun,Jianyu Li +1 more
- 01 Jan 2023
TL;DR: Random matrix-based genetic algorithm (RMGA) is an efficient yet privacy-preserving optimization method that effectively solves privacy-preserving optimization problems (PPOPs) based on a limited number of fitness-preserving evaluations.
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