An Elite Gene Guided Reproduction Operator for Many-Objective Optimization

Zhu, Qingling; Lin, Qiuzhen; Li, Jianqiang; Coello, Carlos A. Coello; Ming, Zhong; Zhang, Mengjie

doi:10.1109/tcyb.2019.2932451

Cited by 15 publications

(4 citation statements)

References 48 publications

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“…2) This paper proposes a mating selection strategy to generate more high-quality offsprings. In the highdimensional objective space, the choice of parents from the entire population is not a good idea since many inefficient offsprings will be created [37], [38]. To improve effects of the genetic operation, the proposed mating selection strategy can produce more efficient ε ε εfeasible solutions.…”

Section: Discussionmentioning

confidence: 99%

Handling Constrained Many-Objective Optimization Problems via Problem Transformation

Jiao

Zeng

et al. 2021

IEEE Trans. Cybern.

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Objectives optimization and constraints satisfaction are two equally important goals to solve constrained manyobjective optimization problems (CMaOPs). However, most existing studies for CMaOPs can be classified as feasibilitydriven constrained many-objective evolutionary algorithms (C-MaOEAs), they always give priority to satisfy constraints, while ignoring the maintenance of the population diversity for dealing with conflicting objectives. Consequently, the population may be pushed towards some locally feasible optimal or locally infeasible areas in the high-dimensional objective space. To alleviate this issue, this paper presents a problem transformation technique, which transforms a CMaOP into a dynamic CMaOP (DCMaOP) for handling constraints and optimizing objectives simultaneously, to help the population cross the large and discrete infeasible regions. The well-known reference-point-based NSGA-III is tailored under the problem transformation model to solve CMaOPs, namely DCNSGA-III. In this paper, ε ε ε-feasible solutions play an important role in the proposed algorithm. To this end, in DCNSGA-III, a mating selection mechanism and an environmental selection operator are designed to generate and choose high-quality ε ε ε-feasible offspring solutions, respectively. The proposed algorithm is evaluated on a series of benchmark CMaOPs with 3, 5, 8, 10, and 15 objectives and compared against six state-of-the-art CMaOEAs. The experimental results indicate that the proposed algorithm is highly competitive for solving CMaOPs.

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Section: Discussionmentioning

confidence: 99%

Handling Constrained Many-Objective Optimization Problems via Problem Transformation

Jiao

Zeng

et al. 2021

IEEE Trans. Cybern.

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“…According to Jiao et al [38], the DCNSGA-III method is proposed, which is created by employing reference-point-based NSGA-III to resolve constrained multi-objective optimization problems (CMaOPs). The NSGA-III method is advantageous to handle constraints and optimize objectives concurrently, which can make a CMaOP dynamic [59]. The DCNSGA-III method performs great competitiveness compared with other sophisticated CMaOEAs and is competent for solving CMaOPs.…”

Section: The Dcnsga-iii Algorithmmentioning

confidence: 99%

Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures

Zhang,

Yu,

et al. 2023

Applied Sciences

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Thin-walled structures, when used for high-speed railways, can effectively mitigate the irreversible destruction when a malfunction occurs. Nested thin-walled tubes, as energy-absorbing structures, possess excellent specific energy absorption (SEA) and crushing force efficiency (CFE). This paper conducts multi-objective optimization by focusing on a square corrugation nested structure with a double octagon inner wall, namely SCOD, to ameliorate the crashworthiness of the nested structure. The finite element model of the SCOD is constructed and validated by test data. A set of experimental design points with good spatial distribution are obtained using the optimal Latin hypercube (LHC) method. The polynomial response surface (PRS) method was applied to establish the fitting relationship between design variables and optimization objectives, and validation is accomplished. The DCNSGA-III algorithm is employed for optimization, resulting in a Pareto alternative solution set with good population diversity and convergence. In addition, to observe the optimized performance, a set of optimal solutions considering a single objective value is derived, and a comprehensive optimal solution is obtained by applying the minimum distance selection method (TMDSM). Finally, the proposed optimized system is analyzed and validated. According to the alternative reference solutions, the initial peak force (IPCF) reduces by 53.75% and CFE increases by 8.7%. This paper provides some reference for the optimization design in practical engineering.

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“…In many-objective problems, the limited size of the population causes solutions to be far apart from each other [47]. In such a population, two distant parent solutions are likely to produce offspring solutions that are also distant from parents [48]. In this scenario, the effect of the recombination operator questionable.…”

Section: A Impact Of Drssmentioning

confidence: 99%

Solving Many-Objective Optimization Problems by a Pareto-Based Evolutionary Algorithm With Preprocessing and a Penalty Mechanism

Liu

Zhu

2021

IEEE Trans. Cybern.

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An Elite Gene Guided Reproduction Operator for Many-Objective Optimization

Cited by 15 publications

References 48 publications

Handling Constrained Many-Objective Optimization Problems via Problem Transformation

Handling Constrained Many-Objective Optimization Problems via Problem Transformation

Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures

Solving Many-Objective Optimization Problems by a Pareto-Based Evolutionary Algorithm With Preprocessing and a Penalty Mechanism

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