2023
DOI: 10.1016/j.swevo.2023.101281
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A dynamic multi-objective evolutionary algorithm using adaptive reference vector and linear prediction

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Cited by 9 publications
(3 citation statements)
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“…Dynamic multi-objective optimization problems (DMOPs) find applications across various industrial production scenarios [1][2][3] . Due to dynamic and uncertain factors, the Pareto Set and Pareto Front in these problems are not constant, making them a focal point of recent research.…”
Section: Introductionmentioning
confidence: 99%
“…Dynamic multi-objective optimization problems (DMOPs) find applications across various industrial production scenarios [1][2][3] . Due to dynamic and uncertain factors, the Pareto Set and Pareto Front in these problems are not constant, making them a focal point of recent research.…”
Section: Introductionmentioning
confidence: 99%
“…To address above basic and important issue in handling DMOPs, the existing DMOAs can be generally divided into the memory- [45], [59], prediction- [42], [48]- [50], [57], [70], and diversity-based methods [12], [35], [36]. Additionally, some hybrid algorithms have also been proposed [8], [21], [31], [53], [69], and in particular, a novel trend of developing DMOAs has emerged recently, which combines the memory mechanism and the prediction method, where the transfer learning (TL) technique has been adopted to make full use of history knowledge to accelerate convergence in a new environment, see [25], [26], [33], [39], [55], [60] for some successful applications.…”
Section: Introductionmentioning
confidence: 99%
“…From the experiments, it is found that the proposed algorithm can well balance the convergence and diversity. Similarly, an adaptive reference vector-based adjustment strategy has been introduced in [70] along with a linear prediction strategy, whose effectiveness is demonstrated on twelve functions with diverse dynamic characteristics.…”
Section: Introductionmentioning
confidence: 99%