A Surrogate-Assisted Cooperative Co-evolutionary Algorithm for Solving High Dimensional, Expensive and Black Box Optimization Problems

Blanchard, Julien; Beauthier, Charlotte; Carletti, Timotéo

doi:10.1007/978-3-319-97773-7_4

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…As various EOPs have different characteristics, using suitable optimization framework and paradigm can solve the targeted problem in a more efficient way. For solving various EOPs, commonly seen optimization frameworks include multi-population/multi-swarm evolution [86,87] , coevolution [88−90] , decomposition-based evolution [63,91] , while widely used optimization paradigms involve single-object- Fitness approximation MGP-SLPSO [36] Expensive, highdimensional…”

Section: Optimization Framework and Paradigmmentioning

confidence: 99%

Evolutionary Computation for Expensive Optimization: A Survey

Zhan

Zhang

2022

Mach. Intell. Res.

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Expensive 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 solution. 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.

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Section: Optimization Framework and Paradigmmentioning

confidence: 99%

Evolutionary Computation for Expensive Optimization: A Survey

Zhan

Zhang

2022

Mach. Intell. Res.

View full text Add to dashboard Cite

show abstract

“…A higher dimensionality of the problem under consideration also requires further improvements for the optimization algorithm itself. Sophisticated evolutionary methods are receiving attention in recent years, such as cooperative PSOs [200], hierarchical PSOs [205] or Cooperative Co-evolutionary approaches [206]. The applicability of LSGO techniques should also be inspected in this regard in combination with the aforementioned Deep Learning surrogates.…”

Section: Surrogate Model Assisted Optimizationmentioning

confidence: 99%