2017
DOI: 10.1016/j.asoc.2017.05.008
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The effect of diversity maintenance on prediction in dynamic multi-objective optimization

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Cited by 146 publications
(45 citation statements)
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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%
“…. 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%
“…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%
“…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%