Article
Computer Science, Artificial Intelligence
Zhiwei Xu, Kai Zhang
Summary: Inspired by the multitasking capability of human brains, evolutionary multitasking and immune algorithm are proposed to improve the efficiency of optimizing multiple tasks. A novel multiobjective multifactorial immune algorithm with information transfer method shows promising performances in solving multiobjective multitask optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Management
Salvatore Corrente, Salvatore Greco, Benedetto Matarazzo, Roman Slowinski
Summary: In this paper, we propose an interactive evolutionary multiobjective optimization (IEMO) approach guided by a preference elicitation procedure inspired by artificial intelligence and decision psychology. The approach utilizes decision rules to influence the optimization process and has been proven to converge to the most interesting part of the Pareto front.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Luan, Junjiang He, Jingmin Yang, Xiaolong Lan, Geying Yang
Summary: This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address the common challenge faced by many existing algorithms in solving real-world optimization problems. By employing strategies such as uniform initialization and self-organizing map, the algorithm improves the population diversity and uniformity. Comparative analysis with 13 other algorithms validates the superiority of the proposed algorithm in terms of uniformity and objective function balance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fangqing Gu, Haosen Liu, Yiu-ming Cheung, Hai -Lin Liu
Summary: This study proposes an adaptive constraint regulation method to balance the feasibility and convergence of solutions by adjusting the constraint violation of infeasible solutions. Experimental results demonstrate that the proposed method effectively achieves solution balance and improves solution diversity.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yatong Chang, Wenjian Luo, Xin Lin, Zhen Song, Carlos A. Coello Coello
Summary: This paper proposes the definition of the biparty multiobjective optimal power flow (BPMOOPF) problem and introduces a novel evolutionary biparty multiobjective optimization algorithm (BPMOOPF-EA) to solve the problem. Experimental results show that BPMOOPF-EA outperforms other algorithms in solving the MOOPF problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Kunjie Yu, Boyang Qu, Jing Liang, Caitong Yue, Xuanxuan Ban
Summary: In this article, an evolution-based constrained multiobjective feature extraction method (ECMOFE) is proposed, which leverages the information generated in the evolutionary process to form the feature matrix. Two populations are created to optimize constraints and objectives, and two complementary evolutionary operators are used to generate offspring for each population. The successful rate of offspring individuals generated by each operator of each population is recorded to form the feature matrix. Based on the formed features, several algorithm recommendation methods are built on the basis of classifiers. The results based on multiple metrics show the effectiveness of the proposed ECMOFE.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Wu Lin, Zexuan Zhu, Maoguo Gong, Jianqiang Li, Carlos A. Coello Coello
Summary: This article proposes a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces to maintain diversity in solutions. Experimental results validate the advantages of this approach in maintaining diversity in both objective and decision spaces.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Yuan Liu, Yikun Hu, Ningbo Zhu, Kenli Li, Juan Zou, Miqing Li
Summary: Recently, decomposition-based multiobjective evolutionary algorithms (DMEAs) have become more prevalent than other patterns for solving multiobjective optimization problems. A DMEA with weights updated adaptively (DMEA-WUA) has been developed for problems regarding various Pareto fronts to improve efficiency. The algorithm is suitable for solving problems with various Pareto fronts, including those with regular and irregular shapes.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Wanpeng Zhang, Shuai Wang, Aimin Zhou, Hu Zhang
Summary: This paper proposes a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for multiobjective optimization problems (MOPs). The authors empirically study and optimize the modeling and sampling components of RM-MEDA, resulting in improved performance. Experimental results demonstrate that the optimized algorithm outperforms five state-of-the-art multiobjective evolutionary algorithms on various benchmark problems.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xin Lin, Wenjian Luo, Naijie Gu, Qingfu Zhang
Summary: This paper investigates the dynamic preferences of decision makers in multiobjective optimization problems and proposes an algorithm framework using a reference point change model. Experimental results show that the algorithm performs well in portfolio optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Lin, Wenjian Luo, Naijie Gu, Qingfu Zhang
Summary: In the field of preference-based evolutionary multiobjective optimization, this paper focuses on multiobjective optimization problems with dynamic preferences of the decision maker (DM). Prior to proposing a change model of the reference point to simulate the change of the preference over time, a dynamic preference-based multiobjective evolutionary algorithm framework is designed. Experimental results on portfolio optimization problems demonstrate the superior performance of the proposed algorithm among compared optimization algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lianghao Li, Cheng He, Wenting Xu, Linqiang Pan
Summary: The proposed pioneer selection strategy effectively handles complex constrained optimization problems with discontinuous feasible regions, by adjusting the ratio of pioneer solutions to approximate the Pareto optimal front. Experimental results demonstrate the effectiveness of the strategy and show that the proposed benchmark problems are challenging for existing approaches.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Songbai Liu, Qiuzhen Lin, Liang Feng, Ka-Chun Wong, Kay Chen Tan
Summary: Evolutionary transfer optimization (ETO) is a hot topic in evolutionary computation, which seeks to improve optimization efficiency by transferring knowledge across related exercises. This article proposes a multitasking ETO algorithm using transfer learning to solve large-scale multiobjective optimization problems (LMOPs). The algorithm utilizes a discriminative reconstruction network (DRN) for each LMOP to transfer solutions, evaluate correlation, and learn a reduced Pareto-optimal subspace of the target LMOP. The effectiveness of the algorithm is validated in real-world and synthetic problem suites.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)