The effect of diversity maintenance on prediction in dynamic multi-objective optimization

Ruan, Gan; Yu, Guo; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang

doi:10.1016/j.asoc.2017.05.008

Cited by 142 publications

(45 citation statements)

References 19 publications

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“…. In a dynamic environment, in some cases, when we compare the performance of different strategies, we need to analyze them at different time periods [34,37,54,57]. In this paper, 100 environmental changes were divided into three stages.…”

Section: Comparison On Performance Evaluation Resultsmentioning

confidence: 99%

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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Section: Comparison On Performance Evaluation Resultsmentioning

confidence: 99%

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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“…It predicts the key points of the optimal front, such as the boundary point, inflection point, etc. Also, the linear model is used by Ruan [22] to predict the position of the optimal front. In maintaining population diversity, the changes in population are predicted based on the extreme point of the previous generation [20].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Dynamic Multi-Objective Optimization Based on Segment and Cloud Prediction

Gao

Song

et al. 2020

Symmetry

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In the real world, multi-objective optimization problems always change over time in most projects. Once the environment changes, the distribution of the optimal solutions would also be changed in decision space. Sometimes, such change may obey the law of symmetry, i.e., the minimum of the objective function in such environment is its maximum in another environment. In such cases, the optimal solutions keep unchanged or vibrate in a small range. However, in most cases, they do not obey the law of symmetry. In order to continue the search that maintains previous search advantages in the changed environment, some prediction strategy would be used to predict the operation position of the Pareto set. Because of this, the segment and multi-directional prediction is proposed in this paper, which consists of three mechanisms. First, by segmenting the optimal solutions set, the prediction about the changes in the distribution of the Pareto front can be ensured. Second, by introducing the cloud theory, the distance error of direction prediction can be offset effectively. Third, by using extra angle search, the angle error of prediction caused by the Pareto set nonlinear variation can also be offset effectively. Finally, eight benchmark problems were used to verify the performance of the proposed algorithm and compared algorithms. The results indicate that the algorithm proposed in this paper has good convergence and distribution, as well as a quick response ability to the changed environment.

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“…Prediction schemes can be used to track the moving optima [15] [16], locations that individuals should be re-initialized to [17], as well as the time when the next change will occur and which possible environments will appear in the next change [18].…”

Section: Introductionmentioning

confidence: 99%

“…Misleading under inaccurate predictions [16]; in need of sufficient training data; inefficient in fast finding/tracking [7] Multi-population Maintaining diversity at the global level [28]; managing multiple optima in parallel; effective on competing peaks and multimodal problems [30] Slow search; generally high computational costs; hard to assign tasks effectively and divide sub-populations appropriately…”

Section: Introductionmentioning

confidence: 99%

Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems

Elsayed

Sarker

et al. 2020

IEEE Access

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In many real-world decision problems, there are specific objective, decision variables, conditions, data and/or parameters that may vary over time. When these problems are solved through an optimization process, they are generally known as dynamic optimization. Dynamic optimization is a challenging research topic as the optimal solution usually moves with any change in the problem environment. In locating the optimal point in any optimization problems, the effectiveness of the search process is influenced by the nature of the problem landscape. In order to improve the effectiveness of the search process, in this paper, a new approach is developed by integrating a landscape-based strategy with appropriately designed evolutionary algorithms for solving dynamic problems. The proposed approach is named as Landscape Influenced Dynamic Optimization Algorithm (LIDOA). LIDOA checks the similarity before and after the changed environment which is then used as an input to guide the evolutionary search process. The dynamic benchmark functions from IEEE-CEC2009 are solved using LIDOA. LIDOA was able to enhance the performance of the evolutionary algorithms in their adaptation to dynamic changes, which in turn enhanced their ability to attain better results.

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The effect of diversity maintenance on prediction in dynamic multi-objective optimization

Cited by 142 publications

References 19 publications

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

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

A New Method for Dynamic Multi-Objective Optimization Based on Segment and Cloud Prediction

Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems

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