A Shift Vector Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Dynamic Optimization

Zhu, Zhen; Tian, Xiang; Xia, Changgao; Chen, Long; Cai, Yingfeng

doi:10.1109/access.2020.2974324

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Finally, the prediction method (predictable variable) uses the center prediction with a Kalman filter. Zhu et al [89] proposed incorporating a shift vector-guided prediction model into an algorithm based on decomposition. The model forecasts a new location for the solution of each subproblem in the new environment.…”

Section: Prediction-based Approachesmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

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Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle more complex and realistic optimizations, such as dynamic multi-objective optimization problems (DMOPs). A dynamic MOEA (DMOEA) can be constructed by changing the MOEA structure and variation operators used to solve DMOPs. Furthermore, DMOEAs can implement change-detection strategies and mechanisms to handle the dynamics of the environment. DMOEAs are often designed to solve unconstrained DMOPs. However, several studies have been conducted to solve dynamic constrained MOPs and more recently, to solve dynamic many-objective optimization problems. There are many real-world DMOPs; however, few DMOEAs have been evaluated with respect to those problems. Evaluation of algorithm performance is an essential aspect of using DMOEAs. Several measures used to validate MOEAs have been adapted to assess DMOEAs and quantitatively compare them. This paper presents a broad review of DMOEAs. The information is summarized and organized according to current research branches. Furthermore, a more general classification scheme for DMOEAs is proposed, based on the method of incorporating diversity to solve DMOPs.

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Section: Prediction-based Approachesmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

show abstract

“…Due to the mutual contradiction among different objectives, it is necessary to find more Paretooptimal solutions (PS) for MOPs. The mappings of PS in the objective space are called the Pareto front (PF) [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Decomposition-Based Multiobjective Evolutionary Algorithm With Genetically Hybrid Differential Evolution Strategy

et al. 2021

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In the decomposition-based multiobjective evolutionary algorithms (MOEA/Ds), a set of subproblems are optimized by using the evolutionary search to exploit the feasible regions. In recent studies of MOEA/Ds, it was found that the design of recombination operators would significantly affect their performances. Therefore, this paper proposes a novel genetically hybrid differential evolution strategy (GHDE) for recombination in MOEA/Ds, which works effectively to strengthen the search capability. Inspired by the existing studies of recombination operators in MOEA/Ds, two composite operator pools are introduced, each of which includes two distinct differential evolution (DE) mutation strategies, one emphasizing convergence and the other focusing on diversity. Regarding each selected operator pool, two DEs are applied on parents' genes to hybridize offspring by adaptive parameters tuning. Moreover, a fitness-rate-rank-based multiarmed bandit (FRRMAB) is embedded into our algorithm to select the best operator pool by collecting their recently achieved fitness improvement rates. After embedding GHDE into an MOEA/D variant with dynamical resource allocation, a variant named MOEA/D-GHDE is presented. Various test multiobjective optimization problems (MOPs), i.e., UF, F test suites, and MOPs with difficult-to-approximate (DtA) PF boundaries, are used to assess performances. Compared to several competitive MOEA/D variants, the comprehensive experiments validate the superiority of our algorithm.

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“…A multiobjective algorithm can compute in parallel and obtain a set of Pareto optimal solutions [36], [37]. The method of optimizing the decision first is more effective than the traditional multi-objective optimization method [38]. Therefore, this paper establishes a multiobjective two-stage disassembly line balancing model with minimum time, economy, energy consumption and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

2020

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Effective disassembly of obsolete products is critical for remanufacturing, recycling and recycling. However, existing disassembly studies have few or no resource constraints, such as limited numbers of disassembly operators and tools. Two-stage disassembly model is used to improve the flexibility to adapt to remanufacturing market demand. Based on existing references, this paper establishes a multiobjective two-stage disassembly model based on two-stage data envelopment analysis (two-stage DEA). The resource-constructed two-stage DEA model in this paper fully considers the dynamic configuration information of the output factors in each stage. To avoid the problem where the overall efficiency is optimal, the research constructs a resource constrained efficiency two-stage disassembly model. First, with the goal of minimum disassembly time, economy, energy consumption and environment, this study proposes eNSGA-II. By using mixed mutation and ecological evolution strategies, a well-distributed noninferior solution set is obtained. Second, the group of 16 disassembly schemes is used as decision-making units (DMUs) of the two-stage DEA. A comparison with Chen's model rankings shows that the model in this paper performs better. DMU 6 has the highest efficiency ranking, which is the best disassembly solution. Finally, the effectiveness of the proposed algorithm is verified by examples. Compared with NSGA-II and SPEA-II, eNSGA-II shows better performance and effectiveness. INDEX TERMS Disassembly, eNSGA-II, remanufacturing, resource constrained, two-stage DEA.

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A Shift Vector Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Dynamic Optimization

Cited by 5 publications

References 43 publications

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Decomposition-Based Multiobjective Evolutionary Algorithm With Genetically Hybrid Differential Evolution Strategy

Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

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