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Evolutionary Computation for Expensive Optimization: A Survey
Jian-Yu Li1,2
Source PublicationMachine Intelligence Research
AbstractExpensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solu-tion. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation (EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.
KeywordExpensive optimization problem evolutionary computation evolutionary algorithm swarm intelligence particle swarm optimization differential evolution
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Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2.Pazhou Laboratory, Guangzhou 510330, China
3.Victoria University, Melbourne 8001, Australia
Recommended Citation
GB/T 7714
Jian-Yu Li. Evolutionary Computation for Expensive Optimization: A Survey[J]. Machine Intelligence Research,2022,19(1):3-23.
APA Jian-Yu Li.(2022).Evolutionary Computation for Expensive Optimization: A Survey.Machine Intelligence Research,19(1),3-23.
MLA Jian-Yu Li."Evolutionary Computation for Expensive Optimization: A Survey".Machine Intelligence Research 19.1(2022):3-23.
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