A decomposition-based evolutionary algorithm for scalable multi/many-objective optimization

Chen, Jiaxin; Ding, Jinliang; Tan, Kay Chen; Chen, Qingda

doi:10.1007/s12293-021-00330-z

Cited by 10 publications

(5 citation statements)

References 63 publications

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“…MOEA/D is one of the main representatives of decomposition methods, where each subproblem is defined by a weight vector, and an aggregation function is used to transform the MOP into multiple scalar problems that will be optimized during the population evolution process [26,27]. The aggregation function in this study uses the Tchebycheff method (TCH), defined in the below Equation ( 27):…”

Section: Decomposition Strategymentioning

confidence: 99%

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

Bu,

Zhu,

et al. 2024

Coatings

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Ship painting, as one of the three pillars of the shipping industry, runs through the whole process of ship construction. However, there are low scheduling efficiency and excessive carbon emissions in the segmental painting process, and optimizing the scheduling method is an important means to achieve the sustainable development of the ship manufacturing industry. To this end, firstly, a low-carbon scheduling mathematical model for the segmented painting workshop is proposed, aiming to reduce carbon emissions and improve the painting efficiency of the segmented painting workshop. Second, an artificial bee colony algorithm designed based on a decomposition strategy (MD/ABC) is proposed to solve the model. In the first stage, five neighborhood switching methods are designed to achieve the global search employed for each solution. In the second stage, the Technique of Ordering the Ideal Solutions (TOPSIS) improves the competition mechanism through the co-evolution between neighboring subproblems and designs the angle to define the relationship between neighboring subproblems to enhance the localized search and improve population quality. The solution exchange strategy is used in the third stage to improve the efficiency of the algorithm. In addition, a two-stage coding method is designed according to the characteristics of the scheduling problem. Finally, the algorithm before and after the improvement and with other algorithms is analyzed using comparative numerical experiments. The experimental results show the effectiveness of the algorithm in solving the low-carbon scheduling problem of ship segmental painting and can provide reliable guidance for the scheduling program of segmented painting workshops in shipyards.

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Section: Decomposition Strategymentioning

confidence: 99%

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

Bu,

Zhu,

et al. 2024

Coatings

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“…Multi-objective evolutionary algorithms (MOEAs) have been proposed for dealing with MOPs, such as the elitist non-dominated sorting genetic algorithm [12] and the multi-objective evolutionary algorithm based on decomposition [53]. However, some studies demonstrated that the performance of traditional MOEAs degenerates when solving MOPs with more than three objectives, and these MOPs are known as many-objective optimization problems (MaOPs) [5,8,52]. Up to now, a lot of many-objective evolutionary algorithms (MaOEAs) have been adopted to solve MaOPs [24,27].…”

Section: Introductionmentioning

confidence: 99%

An ensemble surrogate-assisted adaptive reference point guided evolutionary algorithm for expensive many-objective irregular problem

You

Niu

Tang

et al. 2023

Preprint

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Surrogate-assisted evolutionary algorithms (SAEAs) are one effective method for solving expensive optimization problems. However, there has been little attention to expensive many-objective irregular problems. To address this issue, we propose an ensemble surrogate-assisted adaptive reference point guided evolutionary algorithm for dealing with expensive many-objective irregular problems. Firstly, a reference point adaptation method is adopted in the proposed algorithm to adjust the reference point for calculating indicators and guide the search process. Secondly, the enhanced inverted generational distance (IGD-NS) indicator is improved by using the modified distance to obey the Pareto compliant, which can maintain a balance between convergence and diversity in the population. Thirdly, an infill sampling criterion is designed to select elite individuals for re-evaluation in case the Pareto fronts are irregular. The added elite individuals update the ensemble surrogate model, which is expected to assist the algorithm in efficiently finding the Pareto optimal solutions in a limited computational resource. Finally, experimental results on several benchmark problems demonstrate that the proposed algorithm performs well in solving expensive many-objective optimization problems with irregular and regular Pareto fronts. A real-world application problem also confirms the effectiveness and competitiveness of the proposed algorithm.

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“…Instead, a number of compromise individuals that non-dominant with each other are obtained as the best solutions, which are called Pareto optimal solutions. So far, evolutionary algorithms (EAs) have become the mainstream algorithm [1], [2] for MOPs and MaOPs because…”

Section: Introductionmentioning

confidence: 99%

A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems

Guo

2022

IEEE Access

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The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper.

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A decomposition-based evolutionary algorithm for scalable multi/many-objective optimization

Cited by 10 publications

References 63 publications

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting

An ensemble surrogate-assisted adaptive reference point guided evolutionary algorithm for expensive many-objective irregular problem

A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems

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