A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

Xu, Qian; Xu, Zhanqi; Ma, Tao

doi:10.1109/access.2020.2973670

Cited by 70 publications

(41 citation statements)

References 113 publications

(184 reference statements)

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“…Although many variants of MOEA/D have been proposed [54], [55], for example, MOEA/D-DRA [56], MOEA/D-ACO [57], and MOEA/D-MO [58], we use MOEA/D-DE [59] as a base optimizer of MCEA/D. In addition, we use a nonlinear SVM, simply denoted as SVM in this manuscript.…”

Section: Preliminariesmentioning

confidence: 99%

Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

Sonoda¹,

Nakata²

2021

Preprint

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Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

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Section: Preliminariesmentioning

confidence: 99%

Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

Sonoda¹,

Nakata²

2021

Preprint

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“…After the above reproduction and update procedures, the parameters of FRRMAB are respectively updated by (3)-(5) in line 24, which are used to select the DE operator. Then, in line 25, as suggested in [43], the location parameter F' in the Cauchy distribution can be computed by ( 10)- (11) ' ' (1-) ( )…”

Section: Update Of Parametersmentioning

confidence: 99%

“…To obtain a good approximation on PF for making decisions, multiobjective evolutionary algorithms (MOEAs) have been a popular approach to optimize various MOPs [9]- [11]. In recent decades, a variety of MOEAs have been developed to solve various theoretical optimization problems and even some real-world applications [12]- [16].…”

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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“…In order to solve optimization problems that cannot be solving using traditional mathematical methods, scientists have developed the so called bio-inspired algorithms [25,26] and amongst which is the particle swarm optimization algorithm (PSO) [27][28][29].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

Liu²,

Chen

2021

Front. Phys.

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A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness.

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A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

Cited by 70 publications

References 113 publications

Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

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

A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

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