Article
Computer Science, Information Systems
Yiqiao Cai, Meiqin Cheng, Ying Zhou, Peizhong Liu, Jing-Ming Guo
Summary: In this study, a hybrid evolutionary multitask algorithm (HEMT) is proposed to solve multiobjective vehicle routing problems with time windows (MOVRPTWs). The algorithm simultaneously optimizes multiple similar MOVRPTWs by globally exploring the search space, conducting local searches, and reusing problem-specific knowledge. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hao Xu, A. K. Qin, Siyu Xia
Summary: Evolutionary Multitask Optimization (EMTO) uses evolutionary algorithms (EAs) to solve multiple optimization tasks simultaneously, utilizing knowledge transfer to improve performance. The proposed adaptive EMTO (AEMTO) framework adjusts knowledge transfer in a synergistic way, effectively addressing negative knowledge transfer and enhancing overall performance.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zedong Tang, Maoguo Gong, Yue Wu, Wenfeng Liu, Yu Xie
Summary: The article presents a novel and computationally efficient intertask information transfer strategy by aligning subspaces. By introducing a learnable alignment matrix, it extracts complementary information among different tasks to enhance the performance of solving complicated problems. This method shows superior performance compared to existing evolutionary multitask optimization algorithms in comprehensive experiments.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Jianqiang Li, Junchuang Cai, Tao Sun, Qingling Zhu, Qiuzhen Lin
Summary: In this paper, a multitask-based evolutionary algorithm (MBEA) with knowledge transfer is proposed to solve the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW) in autonomous transportation. The algorithm tackles large-scale VRPSPDTW instances by utilizing multiple auxiliary tasks, facilitating the evolutionary search process. Experimental results demonstrate the effectiveness of the proposed algorithm in dealing with practical VRPSPDTW problems.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang
Summary: This article proposes a block-level knowledge transfer (BLKT) framework to overcome the limitations of knowledge transfer in multitask optimization problems. BLKT divides individuals into blocks and transfers knowledge at the block-level, enabling transfer between similar dimensions belonging to the same or different tasks. Extensive experiments show that BLKT-based differential evolution outperforms state-of-the-art algorithms in multitask optimization and also achieves competitive performance in single-task global optimization.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Haoyuan Lv, Ruochen Liu
Summary: This article discusses the challenges in evolutionary multitasking optimization and proposes a new method based on adaptive seed transfer. The method achieves knowledge transfer and optimization in multitasking environments through strategies such as dimension unification, adaptive task selection, and task transfer. Experimental results indicate that the proposed method performs competitively.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Honggui Han, Xing Bai, Huayun Han, Ying Hou, Junfei Qiao
Summary: This article proposes a self-adjusting multitask particle swarm optimization algorithm to address the problem of negative transfer in multitask optimization. By designing an effective knowledge estimation metric and a self-adjusting knowledge transfer mechanism, the algorithm achieves effective knowledge transfer and removes ineffective knowledge. Convergence analysis is provided to guarantee the effectiveness of the algorithm. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in suppressing negative transfer and achieving convergence.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Eneko Osaba, Javier Del Ser, Ponnuthurai N. Suganthan
Summary: This article highlights the importance of Transfer Optimization in the Swarm and Evolutionary Computation community. It discusses the critical issues and challenges that need to be addressed in recent research and emphasizes the need for efforts to keep the future of this field on the right track. The ultimate goal is to identify gaps in the current literature and encourage further research to achieve valuable advances in this area.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Review
Computer Science, Artificial Intelligence
Tingyang Wei, Shibin Wang, Jinghui Zhong, Dong Liu, Jun Zhang
Summary: This paper presents a detailed exposition on the research in the field of evolutionary multitask optimization (EMTO), revealing the core components of EMTO algorithms and the fusion between EMTO and traditional evolutionary algorithms. By analyzing the associations of different strategies in various branches of EMTO, this review uncovers research trends and potentially important directions, as well as mentions interesting real-world applications.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Honggui Han, Xing Bai, Hongyan Yang, Ying Hou, Junfei Qiao
Summary: In this paper, a multitask particle swarm optimization algorithm with a dynamic transformation strategy (MTPSO-DTS) is proposed to improve the performance of multitask optimization. By dynamically assessing the similarity among different tasks and assisting dimension transformation, the MTPSO-DTS algorithm achieves a uniform representation of knowledge with different dimensions. The experimental results demonstrate that the MTPSO-DTS algorithm facilitates knowledge transfer among tasks with different dimensions and promotes parallel optimization of multiple tasks.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Aritz D. Martinez, Javier Del Ser, Eneko Osaba, Francisco Herrera
Summary: This paper introduces an adaptive multitask reinforcement learning algorithm called A-MFEA-RL, which improves performance by facilitating the exchange of genetic material through crossover and inheritance mechanisms. Experimental results show that A-MFEA-RL achieves high success rates when handling multiple tasks and enhances knowledge exchange among tasks.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Hongyan Chen, Hai-Lin Liu, Fangqing Gu, Kay Chen Tan
Summary: Multiobjective multitask optimization (MMO) aims to solve multiple problems simultaneously. This study proposes an MMO algorithm that uses transfer rank and a KNN model to achieve this goal. The algorithm introduces the concept of transfer rank to quantify the priority of transfer solutions and improve the likelihood of positive results. Solutions are sorted based on transfer rank, with higher-ranked solutions assumed to be more suitable for transfer. The algorithm also prioritizes previous and positive-transfer solutions and uses a KNN model classifier to distinguish solutions with the same transfer rank. Experimental results demonstrate that the proposed algorithm is more effective than other conventional MMO techniques.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Ta Bao Thang, Nguyen Binh Long, Ngo Viet Hoang, Huynh Thi Thanh Binh
Summary: This paper introduces the concept of the Internet of Things and the Minimum Routing Cost Clustered Tree Problem (CluMRCT), proposing a method using Multifactorial Evolutionary Algorithm (MFEA) to solve the problem. By introducing the improved framework MFEA-II, multiple CluMRCT problems can be effectively solved, enhancing algorithm performance.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Eneko Osaba, Javier Del Ser, Aritz D. Martinez, Amir Hussain
Summary: This survey explores multitasking in the context of solving multiple optimization problems simultaneously. It focuses on dynamically exploiting complementarities among the tasks and using biologically inspired concepts to tackle multitask optimization scenarios. The survey collects and examines the abundant literature in evolutionary multitasking, emphasizing the methodological patterns followed in designing new algorithmic proposals. It also identifies open challenges and promising research directions for leveraging biologically inspired algorithms in multitask optimization.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Jian-Yu Li, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang
Summary: Knowledge transfer plays a vital role in solving multitask optimization problems. This article proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm, which efficiently solves MTOPs using a more general approach. By transferring meta-knowledge, the MKTDE algorithm effectively associates different tasks' heterogeneous multisource data to solve MTOPs more efficiently. Two novel methods, multiple populations for the multiple tasks framework and elite solution transfer, further enhance the MKTDE algorithm. Extensive experiments validate the superior performance of the proposed algorithm compared to state-of-the-art approaches.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yixian Li, Jinghui Zhong
Summary: The study proposes an efficient heterogeneous asynchronous parallel surrogate-assisted evolutionary algorithm (HAS-EA) that improves the search performance of expensive optimization problems. By performing operations in parallel on both CPU and GPU, the algorithm accelerates the search performance.
Article
Computer Science, Artificial Intelligence
Feng-Feng Wei, Wei-Neng Chen, Qing Li, Sang-Woon Jeon, Jun Zhang
Summary: This article defines distributed expensive constrained optimization problems (DECOPs) and proposes a distributed evolutionary constrained optimization algorithm with on-demand evaluation (DEAOE). DEAOE adaptively evolves different constraints in an asynchronous way through on-demand evaluation, improving population convergence and diversity. Experimental results demonstrate that DEAOE outperforms centralized state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) in terms of performance and efficiency.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Civil
Wanyi Zhou, Xiaolin Xiao, Yue-Jiao Gong, Jia Chen, Jun Fang, Naiqiang Tan, Nan Ma, Qun Li, Chai Hua, Sang-Woon Jeon, Jun Zhang
Summary: This study proposes a method of formulating the traffic network as a temporal attributed graph and performing node representation learning on it to address the problem of travel time estimation. The learned representation can jointly exploit dynamic traffic conditions and the topology of the road network, and estimate travel time using a route-based spatio-temporal dependence learning module.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Yue-Jiao Gong, Ting Huang, Yi-Ning Ma, Sang-Woon Jeon, Jun Zhang
Summary: This paper focuses on the multiple-trajectory planning problem for automatic underwater vehicles (AUVs) and proposes a comprehensive model that considers the complexity of underwater environments, efficiency of each trajectory, and diversity among different trajectories. To solve this problem, an ant colony-based trajectory optimizer is developed, incorporating a niching strategy, decayed alarm pheromone measure, and diversified heuristic measure for improved search effectiveness and efficiency. Experimental results demonstrate that the proposed algorithm not only provides multiple AUV trajectories for flexible choice, but also outperforms state-of-the-art algorithms in terms of single trajectory efficiency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang
Summary: This article introduces a differential evolution algorithm based on niche center distinguish (NCD) for multimodal optimization problems. The algorithm uses a genetic algorithm to online solve the NCD problem and proposes a niching and global cooperative mutation strategy that combines niche and population information. Experimental results show that the proposed algorithm performs well in solving multimodal optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Weizhong Wang, Hai-Lin Liu, Kay Chen Tan
Summary: This article proposes a global and local surrogate-assisted differential evolution algorithm (GL-SADE) that utilizes a global RBF model to estimate global trend and accelerate convergence, as well as a local Kriging model to prevent local optima and further exploit the model through a reward search strategy. The algorithm is validated and demonstrated on benchmark functions of varying dimensions and an airfoil optimization problem.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Junna Zhang, Degang Chen, Qiang Yang, Yiqiao Wang, Dong Liu, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a novel differential evolution framework called proximity ranking-based multimodal differential evolution (PRMDE) for multimodal optimization. Through the cooperative cooperation among three main mechanisms, PRMDE is capable of locating multiple global optima simultaneously. Experimental results show that PRMDE is effective and achieves competitive or even better optimization performance than several representative methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Dong Liu, Hao He, Qiang Yang, Yiqiao Wang, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a simple and effective mutation scheme named DE/current-to-rwrand/1 to enhance the optimization ability of differential evolution (DE) in solving complex optimization problems. The proposed mutation strategy, called function value ranking aware differential evolution (FVRADE), balances high diversity and fast convergence of the population. Experimental results demonstrate that FVRADE outperforms several state-of-the-art methods and shows promise in solving real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Yang, Gong-Wei Song, Xu-Dong Gao, Zhen-Yu Lu, Sang-Woon Jeon, Jun Zhang
Summary: High-dimensional optimization problems are becoming increasingly difficult due to interacting variables. This paper presents a random elite ensemble learning swarm optimizer (REELSO) inspired by human observational learning theory to effectively tackle such problems. REELSO partitions the swarm into elite and non-elite groups, allowing non-elite particles to observe and learn from elite particles, promoting convergence and maintaining swarm diversity. Experimental results demonstrate that REELSO performs competitively or even outperforms state-of-the-art approaches for high-dimensional optimization.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junlan Dong, Jinghui Zhong, Wei-Neng Chen, Jun Zhang
Summary: This paper introduces a federated genetic programming framework that can train a global model while protecting data privacy. By processing decentralized data locally without sending the original data to the server, it achieves data privacy protection and reduces data collection time.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ye Tian, Xiaopeng Li, Haiping Ma, Xingyi Zhang, Kay Chen Tan, Yaochu Jin
Summary: This paper proposes a novel operator selection method based on reinforcement learning, which uses deep neural networks to learn a policy that determines the best operator for each parent, addressing the exploration-exploitation dilemma in operator selection.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Feng-Feng Wei, Wei-Neng Chen, Wentao Mao, Xiao-Min Hu, Jun Zhang
Summary: This article proposes an efficient two-stage surrogate-assisted differential evolution (eToSA-DE) algorithm to handle expensive inequality constraints. The algorithm trains a surrogate model for the degree of constraint violation, with the type of surrogate changing during the evolution process. Both types of surrogates are constructed using individuals selected by the boundary training data selection strategy. A feasible exploration strategy is devised to search for promising areas. Extensive experiments demonstrate that the proposed method can achieve satisfactory optimization results and significantly improve the efficiency of the algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Xiao-Fang Liu, Jun Zhang, Jun Wang
Summary: This article presents a cooperative differential evolution algorithm with an attention-based prediction strategy for dynamic multiobjective optimization. Multiple populations are used to optimize multiple objectives and find subparts of the Pareto front. The algorithm achieves a balanced approximation of the Pareto front and adapts to changes in the environment by using a new attention-based prediction strategy. Experimental results demonstrate the superiority of the proposed method to state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Ye Tian, Xingyi Zhang, Cheng He, Kay Chen Tan, Yaochu Jin
Summary: A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, new operators are automatically designed in this work, which are expected to be search space independent and thus exhibit robust performance on different problems.
CHINESE JOURNAL OF ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Yu, Jialu Gao, Jianguo Wei, Jing Li, Kay Chen Tan, Tiejun Huang
Summary: This article proposes a new joint weight-delay plasticity rule, TDP-DL, for improving the learning performance of spiking neural networks (SNNs). By integrating plastic delays into the learning framework, the performance of multispike learning is significantly enhanced. Simulation results demonstrate the effectiveness and efficiency of the TDP-DL rule compared to baseline ones.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
