Solving dynamic multi-objective problems with an evolutionary multi-directional search approach

Hu, Yaru; Ou, Junwei; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang; Ruan, Gan

doi:10.1016/j.knosys.2019.105175

Cited by 11 publications

(7 citation statements)

References 36 publications

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“…Wu et al [153] proposed a directed local search which is along the search direction orthogonal to the moving diretion of the non-dominated sets in two consecutive environments and they showed this new local search than a random local search. Very recently, Hu et al [65] developed a multi-direction local search strategy where population individuals are guided either along the moving direction of nondominated solutions or the opposite direction from the moving direction.…”

Section: Local Searchmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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Section: Local Searchmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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“…Population Prediction Strategy (PPS) [ 49 ] proposed by Zhou et al is used to predict the manifold of the whole search population by using the univariate auto-regression (AR) model. Besides that, many other prediction approach have been proposed in different ways, such as multi-directions [ 50 ], knee points [ 51 ], center points [ 52 ], and boundary points [ 53 ].…”

Section: Related Workmentioning

confidence: 99%

“…The second performance indicator is Hypervolume (HV) [ 48 , 53 ], which is a important metric for evaluating solutions. Different from the other indicators mentioned above, HV needs to set a reference vector dominated by any points in the .…”

Section: Experimental Studiesmentioning

confidence: 99%

Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

et al. 2021

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This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

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“…• Diversity introduction [13] [56] [57]: As its name suggests, this method is to explicitly introduce diversity to the population whenever there is an environmental change.…”

Section: Existing Dmoeasmentioning

confidence: 99%