Article
Multidisciplinary Sciences
Mingwei Fan, Jianhong Chen, Zuanjia Xie, Haibin Ouyang, Steven Li, Liqun Gao
Summary: In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to enhance the search performance for practical multi-objective nutrition decision problems. The algorithm utilizes a neighborhood intimacy factor and a new Gaussian mutation strategy to improve diversity and local search ability. Experimental results show that the proposed algorithm achieves better search capability and obtains competitive results compared to other multi-objective algorithms.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Multidisciplinary
Zhiyuan Dai, Tian Li, Weihua Zhang, Jiye Zhang
Summary: This study reviews the current state and progress of the aerodynamic multi-objective optimization of high-speed trains (HSTs), focusing on the impact of train nose shape parameters, parameterization methods, optimization algorithms, and surrogate models. The study also proposes future research directions in the field of aerodynamic optimization for HSTs.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Minshi Chen, Xin Xu, Gary G. Yen
Summary: The paper proposes an evolution strategy MMO-MOES for solving multimodal multi-objective optimization problems, focusing on searching for multiple groups of optimal solutions in decision space. By using a novel niching strategy and requiring a small population size, MMO-MOES is effective in finding well-distributed and well-converged Pareto optimal solutions. Experimental results show exceptional performance compared to leading-edge MMOEAs in various test problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Hafiz Tayyab Rauf, Jiechao Gao, Ahmad Almadhor, Ali Haider, Yu-Dong Zhang, Fadi Al-Turjman
Summary: A novel variant of differential evolution called MPC-DE is proposed to solve multi-model and multi-objective optimization problems. It utilizes multiple selection strategies and chaotic mapping methods for population initialization and mutation. The performance of MPC-DE is evaluated on benchmark problems and compared with recent DE variants, showing superior results for multi-objective optimization problems and the economic load dispatch problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yupeng Han, Hu Peng, Changrong Mei, Lianglin Cao, Changshou Deng, Hui Wang, Zhijian Wu
Summary: This paper proposes a new multistrategy multiobjective differential evolutionary algorithm, RLMMDE, to solve the exploration and exploitation dilemma in multiobjective optimization problems (MOPs). The algorithm utilizes a multistrategy and multicrossover DE optimizer, an adaptive reference point activation mechanism based on RL, and a reference point adaptation method. Experimental results show that RLMMDE outperforms some advanced MOEAs on benchmark test suites and practical mixed-variable optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Bing-Jie Liu, Xiao-Jun Bi
Summary: This paper proposes an adaptive epsilon-constraint multi-objective evolutionary algorithm, which improves the distribution and convergence of the obtained solution set in constrained multi-objective optimization problems by designing adaptive constraint and mutation strategies, and adopting a replacement mechanism to maximize population diversity and convergence.
Article
Computer Science, Interdisciplinary Applications
Lue Tao, Yun Dong, Weihua Chen, Yang Yang, Lijie Su, Qingxin Guo, Gongshu Wang
Summary: This study addresses a new variant of the assembly line feeding problem in automobile manufacturing, proposing a novel mathematical model and algorithm that achieve superior cost savings, solution quality, and convergence efficiency while providing decision support for managers.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Gaurav Dhiman, Krishna Kant Singh, Mukesh Soni, Atulya Nagar, Mohammad Dehghani, Adam Slowik, Amandeep Kaur, Ashutosh Sharma, Essam H. Houssein, Korhan Cengiz
Summary: The study introduces the Multi-objective Seagull Optimization Algorithm (MOSOA) by extending the previously developed Seagull Optimization Algorithm (SOA). The algorithm utilizes a dynamic archive to cache non-dominated Pareto optimal solutions and employs a roulette wheel selection approach. Testing with benchmark functions shows its superiority over existing metaheuristic algorithms, especially in high convergence Pareto optimal solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: By identifying relevant features, feature selection methods can maintain or improve classification accuracy and reduce dimensionality. This paper proposes a diversity-based multi-objective differential evolution approach to effectively handle the trade-offs between convergence and diversity. The method detects and removes irrelevant and weakly relevant features to reduce the search space and proposes a new binary mutation operator to produce better feature subsets. Experimental results show that the proposed method outperforms current popular multi-objective feature selection methods on 14 datasets with varying difficulty.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Hongyuan Wang, Jingcheng Wang, Yaqi Zhao, Haotian Xu
Summary: This paper focuses on the geological adaptive control of tunneling boring machine (TBM) by using clustering analysis to identify geological types and solving a multi-objective optimization problem. The proposed method improves the performance of TBM in tunneling operation under different geological conditions.
Article
Computer Science, Artificial Intelligence
Vibhu Trivedi, Manojkumar Ramteke
Summary: A new hybrid variant of multi-objective differential evolution algorithm is developed in this study, which combines the abilities of DE/rand/1 strategy and adaptive social evolution algorithm to improve convergence speed and effectiveness. The algorithm outperforms other established algorithms in solving computationally intensive multi-objective optimization problems, showing better convergence with a relatively simple structure and no additional computational cost needed.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Nianyin Zeng, Dandan Song, Han Li, Yancheng You, Yurong Liu, Fuad E. Alsaadi
Summary: The paper proposes a competitive mechanism integrated whale optimization algorithm (CMWOA) for multi-objective optimization problems. By introducing a novel competitive mechanism and improving the calculation of crowding distance, the convergence and accuracy of the algorithm are enhanced. Additionally, concatenating differential evolution (DE) into the population with different adjusting strategies for key parameters further improves the overall performance.
Article
Computer Science, Artificial Intelligence
Mingming Xia, Minggang Dong
Summary: This paper proposes a novel two-archive evolutionary algorithm for constrained multi-objective optimization problems with small feasible regions. The algorithm achieves a balance between convergence, diversity, and feasibility through mechanisms such as cooperation-based mating selection, high-quality solution selection, dynamic selection strategy, and ideal point replacement. Comprehensive experiments demonstrate the superiority of the proposed algorithm in terms of increment p and hypervolume compared to state-of-the-art algorithms.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Kai Zhang, Chaonan Shen, Gary G. Yen
Summary: This article proposes a multipopulation-based differential evolution algorithm, called LSMaODE, to efficiently and effectively solve large-scale many-objective optimization problems. The algorithm divides the population into two groups of subpopulations and applies different optimization strategies. The performance is evaluated in both decision and objective dimensions.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
