A Multiobjective Evolutionary Algorithm Based on Objective-Space Localization Selection

Zhou, Yuejiao; Chen, Zefeng; Huang, Zhengxin; Xiang, Yi

doi:10.1109/tcyb.2020.3016426

Cited by 13 publications

(4 citation statements)

References 67 publications

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“…Instead of working on the entire search space, another decomposition method is to restrict the improvement region of each subproblem to be a local niche, also known as constrained decomposition (CD) [85]. This idea is similar to some TCH and PBI variants (e.g., [38,77,78,81,82]) that applied a Mating selection MOEA/D-DE [23], MOEA/D-TPN [90], MOEA/D-MRS [91] Neighborhood versus global ENS-MOEA/D [92], MOEA/D-ATO [93] Ensemble neighborhood MO-RCGA [94], SRMMEA [95], MOEA/D-EAM [96] Neighborhood of solutions Environmental selection MOEA/D-DE [23], EFR [97] Neighborhood versus global MOEA/DD [28], ISC-MOEA/D [98], g-DBEA [54], DDEA and DDEA+NS [54] Prioritizing isolated regions VaEA [99], OLS [100], ASEA [101] MOEA/D-STM [102], AOOSTM and AMOSTM [103], MOEA/D-IR [104] Matching solutions and subproblems MOEA/D-ARS [105], MOEA/D-CD and MOEA/D-ACD [85], MOEA/D-GR [85,106] Global replacement θ-DEA [107], MOEA/D-DU and EFR-RR [108], MOEA/D-CSM [109] Local niche competition dedicated parameterization on each subproblem. Specifically, a constrained optimization subproblem in CD is defined as: min…”

Section: Constrained Decompositionmentioning

confidence: 99%

“…Later, the importance of maintaining isolated solutions irregardless of their convergence and feasibility is empirically investigated in [98]. Inspired by MOEA/DD, there have been some further attempts that try to allocate certain survival chance to solutions that are relatively inferior in terms of the convergence but contribute more in terms of population diversity, e.g., [54,99,100,110]. In addition, MOEA/DD is an early work that aims to leverage the complementary effect of dominanceand decomposition-based selection strategies.…”

Section: Environmental Selectionmentioning

confidence: 99%

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Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including weight vector settings, subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

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Section: Constrained Decompositionmentioning

confidence: 99%

Section: Environmental Selectionmentioning

confidence: 99%

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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“…A recombination strategies and two‐archive (one for convergence rate and other for diversity) guided approach for handling multiple conflicting objectives is proposed by Liu et al (2018). Recently, Zhou et al (2020) proposed a new approach to handle MOPs with irregular Pareto front. Ismayilov and Topcuoglu (2020) suggested an approach for solving dynamic workflow scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Handling multiple objectives using k‐means clustering guided multiobjective evolutionary algorithm

Singh

2021

Expert Systems

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Multiobjective optimization problems (MOPs) are very popular these days and have gained continuous research attention. These problems involve a minimum of two conflicting objectives that need to be optimized simultaneously. Multiobjective evolutionary algorithms (MOEAs) optimize these objectives by targeting solutions on the Pareto front. To judge the performance of different MOEAs, the convergence rate and population diversity are used. In order to create a good algorithm with a high convergence rate and diversity, the k‐means clustering algorithm is combined with a real parameter version of the improved environmental adaptation method (IEAM‐RP) in this paper. This is done as IEAM‐RP converges very fast and the k‐means clustering algorithm is used to provide diversity in decision search space. To check the performance of the proposed algorithm, it has been compared with nine state‐of‐the‐art multiobjective algorithms (MOPSOtridist, MOEA/D‐STM, MOEA/D, MOPSO, NSGA‐II, MOPSOses, MOGOA, MODA, MOALO) using standard multiobjective benchmark functions. Further, the performance of the proposed algorithm is compared against IBEA and SMS‐EMOA using walking fish group (WFG) test suit. The experimental results prove the effectiveness of the proposed algorithm.

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“…With the continuous improvement of supply chain theory and the deepening of supply chain research, the selection and evaluation of suppliers are also constantly given new connotation , which has attracted the general attention of the business community and academia (Wu et al, 2020). By summarizing the literature, we can find that the research in this field mainly focuses on the following two aspects: (1) research on supplier selection methods and (2) research on the indicator system of 2 supplier evaluation (Zhou et al, 2020). At present, with the development of the supply chain, the core enterprises in the supply chain have increasingly high requirements for their own suppliers, so the difficulty of supplier selection has also increased (Dai et al, 2021).…”

mentioning

confidence: 99%

A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers

Jiang,

Hu,

Dong

et al. 2024

International Journal of Information Systems and Supply Chain Management

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E-commerce has grown quickly in recent years thanks to advancements in Internet and information technologies. For the majority of consumers, online shopping has emerged as a primary mode of shopping. However, it has become more challenging for businesses to satisfy consumer demand due to their increasingly individualized wants. To address the need for customized products with numerous kinds and small quantities, businesses must rebuild their supply chain systems to increase their efficiency and adaptability. The SI-LSF technique, which employs boosting learning in the target-relative feature space to lower the prediction error and enhance the algorithm's capacity to handle input-output interactions, is validated in this study using a genuine industrial dataset. The study successfully identifies the relationship between sales and sales as well as target-specific features by applying the multi-objective regression integration algorithm based on label-specific features to a real-world supply chain demand scenario.

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A Multiobjective Evolutionary Algorithm Based on Objective-Space Localization Selection

Cited by 13 publications

References 67 publications

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Handling multiple objectives using k‐means clustering guided multiobjective evolutionary algorithm

A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers

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