Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems

Dong, Huachao; Dong, Zuomin

doi:10.1016/j.swevo.2020.100713

Cited by 68 publications

(16 citation statements)

References 41 publications

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“…Dong et. al [55] presented surrogateassisted grey wolf optimization for high-dimensional and computationally expensive black-box problems that featured RBFassisted meta-heuristic exploration. The RBF featured knowledge mining that includes a global search carried out using the grey wolf optimization and a local search strategy combining global and multi-start local exploration.…”

Section: Surrogate-assisted Optimizationmentioning

confidence: 99%

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Chandra¹,

Vardhan²

2022

Preprint

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Advances in parallel and distributed computing have enabled efficient implementation of distributed swarm and evolutionary algorithms for complex and computationally expensive models. Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that do not have such information available, for instance geoscientific landscape evolution models. However, such models are so computationally expensive that even distributed swarm and evolutionary algorithms with the power of parallel computing struggle. We need to incorporate efficient strategies such as surrogate assisted optimisation that further improves their performance; however this becomes a challenge given parallel processing and inter-process communication for implementing surrogate training and prediction. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. Our results demonstrate very promising results for benchmark functions and geoscientific landscape evolution models. We obtain reduction in computationally time while retaining optimisation solution accuracy though the use of surrogates in a parallel computing environment.

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Section: Surrogate-assisted Optimizationmentioning

confidence: 99%

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Chandra¹,

Vardhan²

2022

Preprint

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“…The Pseudo-code of GWO is shown in Algorithm 1. Through the previous analysis of GWO [87], [93], [94], we can find that GWO has the characteristics of strong exploitation ability, but it usually suffers from premature convergence because the top three individuals in the population greatly In this section, the chaotic grey wolf optimization algorithms will be elaborated. There are generally two methods to incorporate chaotic maps into a heuristic algorithm, i.e., using chaotic sequences to substitute the random numbers in the algorithm, or using CLS to perform a local search.…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Yang

et al. 2021

IEEE Access

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As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

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“…According to the feedback status, three different DE mutation operators are employed to generate new offspring, which are further prescreened by a global or local GP model. Dong et al [38] proposed a surrogate-assisted grey wolf optimization algorithm. It conducts a global search and a multi-start local search based on a dynamically updated RBF model.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, it has been verified that hierarchical SAEAs are of great potential in solving high-dimensional expensive problems [34][35][36][37][38][39]. Profiting from its "blessing of uncertainty" [40], the global surrogate model generally helps to smooth out some local optima and thus to reduce the search space, whereas the local surrogate model helps to identify better solutions in the located local promising regions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection

Ren

Guo

Ren

et al. 2021

Complex Intell. Syst.

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By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of hierarchical SAEAs mainly profits from the potential benefit of their global surrogate models known as “blessing of uncertainty” and the high accuracy of local models. However, their performance leaves room for improvement on high-dimensional problems since now it is still challenging to build accurate enough local models due to the huge solution space. Directing against this issue, this study proposes a new hierarchical SAEA by training local surrogate models with the help of the random projection technique. Instead of executing training in the original high-dimensional solution space, the new algorithm first randomly projects training samples onto a set of low-dimensional subspaces, then trains a surrogate model in each subspace, and finally achieves evaluations of candidate solutions by averaging the resulting models. Experimental results on seven benchmark functions of 100 and 200 dimensions demonstrate that random projection can significantly improve the accuracy of local surrogate models and the new proposed hierarchical SAEA possesses an obvious edge over state-of-the-art SAEAs.

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Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems

Cited by 68 publications

References 41 publications

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Surrogate-assisted distributed swarm optimisation for computationally expensive models

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection

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