A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization

Zou, Juan; Li, Qingya; Yang, Shengxiang; Bai, Hui; Zheng, Jinhua

doi:10.1016/j.asoc.2017.08.004

Cited by 137 publications

(51 citation statements)

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“…Although the ability to find and track moving optima precedes any deployment consideration, designing a framework capable of finding robust solutions over time has strong practical merits. Other practical considerations such as dealing with dynamic constraints [88], [89] and multiple conflicting objectives [90], [91] are important topics deserving further investigation. Proof.…”

Section: Discussionmentioning

confidence: 99%

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Yazdani

Omidvar

Branke

et al. 2020

IEEE Trans. Evol. Computat.

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Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of largescale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decompositionbased coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bilevel resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.

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Section: Discussionmentioning

confidence: 99%

Scaling Up Dynamic Optimization Problems: A Divide-and-Conquer Approach

Yazdani

Omidvar

Branke

et al. 2020

IEEE Trans. Evol. Computat.

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“…Then, several nondominated individuals in the last environment were introduced to the predicted population as the memory set. In the end, we adopted a kind of adaptive diversity maintenance strategy, which is similar to that in [46], to keep diversity. The diversity individuals produced by this diversity strategy are called random set.…”

Section: A Hybrid Predictive Strategy Carried Through Simultaneously mentioning

confidence: 99%

“…The prediction strategy based on center point is a widely used strategy [37,38,40,41,45,46,54,55]. The center point is defined as follows:…”

Section: The Prediction Strategy Based On Center Pointmentioning

confidence: 99%

“…In this paper, an adaptive population diversity maintenance strategy similar to that in [46] was adopted. The strategy adaptively introduced a corresponding number of random individuals according to the difficulty of the problem to be solved.…”

Section: Random Setmentioning

confidence: 99%

“…Many researchers try to find points or regions of special significance [42][43][44]. There are many special points that have been found, not only center points, such as boundary points and knee points [45][46][47][48][49][50]. In this paper, a hybrid prediction strategy based on center point and CTI, which can handle DMOPs from both decision space and objective space, is proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

Guo³

et al. 2019

Wireless Communications and Mobile Computing

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There are many issues to consider when integrating 5G networks and the Internet of things to build a future smart city, such as how to schedule resources and how to reduce costs. This has a lot to do with dynamic multiobjective optimization. In order to deal with this kind of problem, it is necessary to design a good processing strategy. Evolutionary algorithm can handle this problem well. The prediction in the dynamic environment has been the very challenging work. In the previous literature, the location and distribution of PF or PS are mostly predicted by the center point. The center point generally refers to the center point of the population in the decision space. However, the center point of the decision space cannot meet the needs of various problems. In fact, there are many points with special meanings in objective space, such as ideal point and CTI. In this paper, a hybrid prediction strategy carried through from both decision space and objective space (DOPS) is proposed to handle all kinds of optimization problems. The prediction in decision space is based on the center point. And the prediction in objective space is based on CTI. In addition, for handling the problems with periodic changes, a kind of memory method is added. Finally, to compensate for the inaccuracy of the prediction in particularly complex problems, a self-adaptive diversity maintenance method is adopted. The proposed strategy was compared with other four state-of-the-art strategies on 13 classic dynamic multiobjective optimization problems (DMOPs). The experimental results show that DOPS is effective in dynamic multiobjective optimization.

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