Surrogate-Assisted Evolutionary Framework for Data-Driven Dynamic Optimization

Cheng

et al. 2021

Section: Discussion On Dop Benchmarksmentioning

confidence: 99%

Section: Baseline Function Referencesmentioning

confidence: 99%

Section: Plotsmentioning

confidence: 99%

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A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

Cheng

et al. 2021

“…After that, the closest clusters are merged until the sum of intra-cluster distances becomes less than the sum of inter-cluster distances [128]. Some clustering methods take the fitness values of individuals into account as well [38], [81], [84]. In these methods, some better individuals usually become cluster heads.…”

Section: E Population Division and Managementmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

Cheng

et al. 2021

“…The surrogate method can be classified into two categories: 1) data-driven method and 2) knowledgedriven method. The data-driven method [29]- [32] aims to build a surrogate model based on the collected historical This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ Fig.…”

mentioning

confidence: 99%

SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems

Zhan

Zhang

2021

The key challenge of expensive optimization problems (EOP) is that evaluating the true fitness value of the solution is computationally expensive. A common method to deal with this issue is to seek for a less expensive surrogate model to replace the original expensive objective function. However, this method also brings in model approximation error. To efficiently solve the EOP, a novel scale-adaptive fitness evaluation (SAFE) method is proposed in this article to directly evaluate the true fitness value of the solution on the original objective function. To reduce the computational cost, the SAFE method uses a set of evaluation methods (EM) with different accuracy scales to cooperatively complete the fitness evaluation process. The basic idea is to adopt the low-accuracy scale EM to fast locate promising regions and utilize the high-accuracy scale EM to refine the solution accuracy. To this aim, two EM switch strategies are proposed in the SAFE method to adaptively control the multiple EMs according to different evolutionary stages and search requirements. Moreover, a neighbor best-based evaluation (NBE) strategy is also put forward to evaluate the solution according to its nearest high-quality evaluated solution, which can further reduce computational cost. Extensive experiments are carried out on the case study of crowdshipping scheduling problem in the smart city to verify the effectiveness and efficiency of the proposed SAFE method, and to investigate the effects of the two EM switch strategies and the NBE strategy. Experimental results show that the proposed SAFE method achieves better solution quality than some baseline and state-of-the-art algorithms, indicating an efficient method for solving EOP with a better balance between solution accuracy and computational cost.