Journal Article10.1109/tevc.2022.3201890
Decomposition-based Multi-objective Optimization Algorithms with Adaptively Adjusting Weight Vectors and Neighborhoods
13
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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm
TL;DR: In this paper , a new boredom-aware dual-resource constrained flexible job shop scheduling problem is investigated, which considers the increase in workers' boredom caused by repetitive job assignments and constructs an efficiency function to characterize the impact of workers’ boredom.
29
Decomposition-based interval multi-objective evolutionary algorithm with adaptive adjustment of weight vectors and neighborhoods
Yaqing Jin,Zhixia Zhang,Liping Xie,Zhihua Cui +3 more
TL;DR: This paper proposes IMOEA/D-AWN, a decomposition-based interval multi-objective evolutionary algorithm with adaptive weight vectors and neighborhoods, to address interval multi-objective optimization problems with uncertainty, outperforming four advanced IMOEAs on 18 test problems.
7
MOEA/D with customized replacement neighborhood and dynamic resource allocation for solving 3L-SDHVRP
Han Li,Genghui Li,Qiaoyong Jiang,Jiashu Wang,Zhenkun Wang +4 more
TL;DR: This paper proposes MOEA/D-RD, a multi-objective evolutionary algorithm for 3L-SDHVRP, addressing the problem's complexity by incorporating customized replacement neighborhoods and dynamic resource allocation, outperforming state-of-the-art methods in numerical experiments.
5
A dual-population-based evolutionary algorithm for multi-objective optimization problems with irregular Pareto fronts
Xiaoyu Zhong,Xiangjuan Yao,Dunwei Gong,Kangjia Qiao,Xingjia Gan,Zhangxiao Li +5 more
2
A Multitask Multiobjective Operation Optimization Method for Coal Mine Integrated Energy System
Dunwei Gong,Xiaoyan Sun,Bohuang Zeng +2 more
TL;DR: A multitask multiobjective operation optimization framework (MO-EAMP) is proposed to optimize coal mine integrated energy systems, combining evolutionary algorithms with mathematical programming to address strong constraints and large scales, achieving better convergence and distribution.
2
References
Decomposition-Based Multiobjective Evolutionary Algorithm With an Ensemble of Neighborhood Sizes
TL;DR: Experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS.
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
Qingfu Zhang,Wudong Liu,Hui Li +2 more
- 18 May 2009
TL;DR: The new version of MOEA/D has been tested on all the CEC09 unconstrained MOP test instances and a strategy for allocating the computational resource to different subproblems in MOEA /D is proposed.
An improved biogeography/complex algorithm based on decomposition for many-objective optimization
TL;DR: Experimental results on both DTLZ and WFG benchmarks problems demonstrate the superiority of the proposed BBO/Complex algorithm in comparison with three state-of-the-art algorithms in terms of both convergence and diversity.