Journal Article10.1109/tevc.2022.3201890
Decomposition-based Multi-objective Optimization Algorithms with Adaptively Adjusting Weight Vectors and Neighborhoods
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TL;DR: In this paper , a decomposition-based multi-objective optimization algorithm with adaptively adjusting weight vectors and neighborhoods (MOEA/D-AAWN) is developed, where the evolutionary direction of each subproblem is analyzed and the Sparsity function (Spa) is proposed to measure the population density on the PF.
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Abstract: The decomposition-based multi-objective optimization algorithm (MOEA/D) is an effective method of solving a multi-objective optimization problem (MOP). The main idea of MOEA/D is that the objectives are weighted through different vectors to form different subproblems, and an optimal solution set is obtained by co-evolution in a certain neighborhood. However, with the increase of objectives, the number of non-dominated solutions increases exponentially, resulting in the deteriorated capability of searching for optimal solutions. In addition, for an optimization problem with the complex Pareto front(PF), the selection pressure of non-dominated solutions is insufficient. To make evolution more efficient, a decomposition-based multi-objective optimization algorithm with adaptively adjusting weight vectors and neighborhoods (MOEA/D-AAWN) is developed in this paper. Firstly, the evolutionary direction of each subproblem is analyzed and the Sparsity function (Spa) is proposed to measure the population density on the PF. By using Spa, a method of generating uniform vectors is presented to improve the diversity of solutions. Besides, a method of adaptively adjusting neighborhoods is given. It adjusts neighborhoods according to the number of iterations and the Spa value of its corresponding subproblem. In this way, the computational resource can be effectively allocated, leading to the improvement in evolutionary efficiency. The proposed algorithm is applied to solve a series of benchmark optimization instances, and the experimental results show that the proposed algorithm outperforms comparison algorithms in runtime, convergence, and diversity.
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Citations
Bi-Level Collaborative Optimization for Medical Consumable Order Splitting and Reorganization Considering Multi-Dimensional and Multi-Scale Characteristics
Peng Jiang,Shunsheng Guo,Xu Luo +2 more
Abstract: Medical consumable orders are characterized by diverse product types, small batch sizes, frequent orders, and high customization requirements, often leading to inefficient workshop scheduling and difficulties in meeting multiple production constraints. To address these challenges, this study proposes a bi-level optimization model for order splitting and reorganization considering multi-dimensional and multi-scale characteristics. The multi-dimensional characteristics encompass materials, processes, equipment, and work efficiency, while the multi-scale aspects involve finished products, components, assemblies, and parts. At the upper level, the model optimizes order task splitting by refining splitting strategies and preprocessing constraints to generate high-quality input for the reorganization phase. The lower level optimizes sub-task prioritization, batch sizes, and resource scheduling to develop a production plan that balances cost and efficiency. Subsequently, to solve this bi-level optimization problem, a hybrid bi-objective optimization algorithm is designed, integrating a collaborative iterative strategy to enhance solution efficiency and quality. Finally, a case study and comparative experiments validate the practicality and effectiveness of the proposed model and algorithm.
Analyzing and Overcoming Local Optima in Complex Multi-Objective Optimization by Decomposition-Based Evolutionary Algorithms
Ting Dong,Haoxin Wang,Hengxi Zhang,Wenbo Ding +3 more
TL;DR: Analyzing and overcoming local optima in complex multi-objective optimization by decomposition-based evolutionary algorithms leads to improved solution diversity and convergence.
An improved MOEA/D with reinforcement learning for flexible job shops incorporating manual operations and fatigue effects
Yibing Li,Xin Tong,Jun Guo,Lei Wang,Hongtao Tang,Kaipu Wang,Xinyu Li +6 more
References
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Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
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Eckart Zitzler,Kalyanmoy Deb,Lothar Thiele +2 more
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TL;DR: In this article, the authors provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions, each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front.
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