Adaptive Replacement Strategies for MOEA/D

Wang, Zhenkun; Zhang, Qingfu; Zhou, Aimin; Gong, Maoguo; Jiao, Licheng

doi:10.1109/tcyb.2015.2403849

Cited by 228 publications

(97 citation statements)

References 35 publications

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“…In MOEA/D, a multi-objective optimization problem (MOP) is transformed into a set of single optimization sub-problems by applying decomposition approaches, and then evolutionary algorithms are utilized to optimize these sub-problems simultaneously. With the use of different decomposition methods and different evolutionary algorithms, various versions of MOEA/D have been developed in recent years, e.g., MOEA/D-DE [22], MOEA/D-DRA [31], MOEA/D-XBS [21], and MOEA/D-GR [23]. Although different variants of MOEA/D are available in literature, a powerful single version of MOEA/D that integrates different advantages of the current versions is not yet in place.…”

Section: Moea/d Algorithmmentioning

confidence: 99%

“…Although different variants of MOEA/D are available in literature, a powerful single version of MOEA/D that integrates different advantages of the current versions is not yet in place. With the aim of developing an efficient version of MOEA/D for real-life problems, a powerful MOEA/D version is therefore developed in this study that is a combination of MOEA/D-DE [22], an adaptive replacement strategy [23], a stopping condition criterion [24], and a constraint-handling technique [25]. The general framework of MOEA/D is presented in Algorithm 1.…”

Section: Moea/d Algorithmmentioning

confidence: 99%

“…The general framework of MOEA/D is presented in Algorithm 1. For more details, readers are encouraged to refer to [22][23][24][25]32].…”

Section: Moea/d Algorithmmentioning

confidence: 99%

“…Furthermore, the efficiency of the MOEA/D version in this paper is also considerably enhanced by the integration of some recently developed advantageous features. These include an adaptive replacement strategy [23], a stopping condition criterion [24], and a constraint-handling technique [25]. Also, to reduce redundant evaluations of infeasible solutions derived from operational constraints during different flight phases, the new trajectory parameterization technique recently proposed in [19] is also applied.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

et al. 2017

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Abstract:In an effort to allow to increase the number of aircraft and airport operations while mitigating their negative impacts (e.g., noise and pollutant emission) on near-airport communities, the optimal design of new departure routes with less noise and fuel consumption becomes more important. In this paper, a multi-objective evolutionary algorithm based on decomposition (MOEA/D), which recently emerged as a potential method for solving multi-objective optimization problems (MOPs), is developed for this kind of problem. First, to minimize aircraft noise for departure routes while taking into account the interests of various stakeholders, bi-objective optimization problems involving noise and fuel consumption are formulated where both the ground track and vertical profile of a departure route are optimized simultaneously. Second, in order to make the design space of vertical profiles feasible during the optimization process, a trajectory parameterization technique recently proposed is employed. Furthermore, some modifications to MOEA/D that are aimed at significantly reducing the computational cost are also introduced. Two different examples of departure routes at Schiphol Airport in the Netherlands are shown to demonstrate the applicability and reliability of the proposed method. The simulation results reveal that the proposed method is an effective and efficient approach for solving this kind of problem.

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Section: Moea/d Algorithmmentioning

confidence: 99%

Section: Moea/d Algorithmmentioning

confidence: 99%

“…The general framework of MOEA/D is presented in Algorithm 1. For more details, readers are encouraged to refer to [22][23][24][25]32].…”

Section: Moea/d Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

et al. 2017

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“…To validate the effectiveness and robustness of LSIAS, 22 well-known benchmark problems such as ZDT problems [28], DTLZ problems [29], and UF problems [30] are adopted. The comparative experiments are demonstrated when the MOEA/D-LSIAS compares with some versions of MOEA/D, for example, MOEA/D-DE [31], MOEA/D-DRA [27], MOEA/D-FRRMAB [16], MOEA/D-STM [32], MOEA/ D-UCB-T [21], and MOEA/D-AGR [33].…”

Section: Introductionmentioning

confidence: 99%

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Gao

Qing-hua

Fan

et al. 2017

Mathematical Problems in Engineering

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The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems.

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Impression space model for the evaluation of Internet advertising effectiveness

Song¹,

Hong²,

Zhang³

et al. 2020

Concurrency and Computation

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The evaluation of Internet advertising effectiveness plays an important role in network marketing at this stage. The evaluation criteria of Internet advertising effectiveness mostly use proceeds as an indicator, ignoring the brand effect generated by Internet advertising, making it difficult for researchers to further study the effect of Internet advertising. With the updating and iteration of technical theory, it is an urgent task to find a new criteria to measure Internet advertising effectiveness. At present, the research on Internet advertising effectiveness research is based on the analysis of nondifferentiation category. The research on user click behavior is a holistic analysis of the whole web page. This global coverage research is difficult to present the behavior characteristics of users in the web page. In response to the above problems, the impression space is proposed to measure Internet advertising effectiveness. Firstly, the fuzzy classification theory is used to digitize the user category data, the user type is divided by probability distribution, and the distribution of web page interest regions is divided by gaze time and page layout to further study the click-and-effect relationship of different users and the user browsing time series. Then, establish multimodal multifactors to build an impression space model to measure Internet advertising effectiveness. The experimental results show that the accuracy of the constructed impression space model for the evaluation of Internet advertising effectiveness reaches 92.4%, and the impression space is more effective as the evaluation of Internet advertising effectiveness. K E Y W O R D S area of interest, impression space model, the fuzzy classification theory Concurrency Computat Pract Exper. 2020;32:e5678.wileyonlinelibrary.com/journal/cpe

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Adaptive Replacement Strategies for MOEA/D

Cited by 228 publications

References 35 publications

An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

An Efficient Application of the MOEA/D Algorithm for Designing Noise Abatement Departure Trajectories

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Impression space model for the evaluation of Internet advertising effectiveness

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