Article
Computer Science, Artificial Intelligence
Lingjie Li, Qiuzhen Lin, Ke Li, Zhong Ming
Summary: A novel vertical distance-based clonal selection mechanism (VD-MOIA) is proposed in this study to improve population diversity in MOIAs. By decomposing the target MOP into a set of subproblems and executing the vertical distance-based clonal selection mechanism, good results are achieved in multiobjective optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Yi Wang, Tao Li, Xiaojie Liu, Jian Yao
Summary: This study develops an improved adaptive clonal selection algorithm with multiple differential evolution strategies. The algorithm introduces an adaptive mutation strategy pool, an adaptive population resizing method, and detection methods for premature convergence and stagnation. Experimental results demonstrate that the proposed method outperforms state-of-the-art clonal selection algorithms and differential evolution algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hongling Chen, Yanyan Tan, Zeyuan Yan, Lili Meng, Wenbo Wan
Summary: This paper proposes a hybrid operator selection strategy based on feedback and support vector machine (SVM) classification for improving the multiobjective optimization algorithm. Experimental results verify the effectiveness of this strategy.
Article
Computer Science, Artificial Intelligence
Shijie Xiong, Wenyin Gong, Kai Wang
Summary: This paper proposes an enhanced adaptive neighborhood-based speciation differential evolution (EANSDE) algorithm to solve multimodal optimization problems (MMOPs). The algorithm adaptively controls parameters to alleviate the fine-tuning process by users. It introduces an external archive to store inferior solutions and merges them with the current population in the following search. Additionally, a crowding relieving mechanism is proposed to remove extremely similar individuals from the population. Experimental results demonstrate the superiority of EANSDE on the 20 benchmark MMOPs in CEC-2013.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Environmental Sciences
Yilin Chen, Bo Gao, Tao Lu, Hui Li, Yiqi Wu, Dejun Zhang, Xiangyun Liao
Summary: This article presents an improved dragonfly algorithm combined with a directed differential operator for feature selection. By adaptively adjusting the step size, designing a new differential operator, and updating the directed differential operator, the proposed method enhances the search capability and convergence speed. Experimental results demonstrate that the proposed algorithm outperforms other representative algorithms in terms of both convergence speed and solution quality.
Article
Multidisciplinary Sciences
Kaifeng Geng, Li Liu, Zhanyong Wu
Summary: This study considers the distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent setup times, considering factory eligibility constraints under time of use price. It proposes a multi-objective Artificial Bee Colony Algorithm to optimize both the makespan and total energy consumption. The algorithm demonstrates its effectiveness in solving the scheduling problem through extensive experiments.
SCIENTIFIC REPORTS
(2022)
Article
Energy & Fuels
N. Ding, K. Prasad, T. T. Lie
Summary: This paper introduces a hybrid EMS system for PHEV, using a rule-based control strategy and genetic algorithm optimization technique to overcome battery limitations. Simulation studies have shown significant improvements in emissions control with the proposed system.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Junqi Liu, Zeqiang Zhang, Feng Chen, Silu Liu, Lixia Zhu
Summary: This paper addresses the lack of research on relationship constraints between facilities in the corridor allocation problem (CAP). It proposes an immune clone selection algorithm with variable neighborhood operation (ICSAVNS) to solve this problem. The algorithm improves the quality of initial solutions, accuracy of local search, and achieves population compression through the use of a double index sequence. Experimental results demonstrate the superior performance of the proposed algorithm in solving this problem.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Engineering, Electrical & Electronic
Yufei Wang, Donglin Liu, Yu Wu, Hua Xue, Yang Mi
Summary: This paper proposes a locating and sizing method of charging station based on the neighborhood mutation immune clone selection algorithm to solve the location problem caused by the popularity of electric vehicles. The method uses K-MEANS clustering to analyze the randomness of electric vehicles charging load. The distance between EVs and charging stations and the capacity of charging stations are combined to determine the service range. An improved immune clonal selection algorithm with neighborhood mutation is proposed for the iterative solution of the planning model. MATLAB analysis verifies the effectiveness of the model and algorithm, showing improved optimization accuracy compared to traditional algorithms.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Geochemistry & Geophysics
Chao Chen, Yuting Wan, Ailong Ma, Liangpei Zhang, Yanfei Zhong
Summary: Feature selection is an effective method for handling the correlation of hyperspectral image data. However, existing methods may suffer from inefficiency and loss of search space in high-dimensional and multipeak search spaces. This article proposes a novel feature selection algorithm based on decomposition and clonal selection, which achieves superior classification performance with multiobjective optimization.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Yongbin Zhu, Tao Li, Xiaolong Lan
Summary: Improving classification performance is crucial for practical applications, and feature selection is an important preprocessing step in machine learning systems. However, existing methods based on heuristic search strategies often have high running costs. This paper proposes an efficient feature selection method based on artificial immune algorithm optimization, which introduces a clone selection algorithm and genetic shuffling technology to improve search performance. Experimental results show that this method achieves better classification accuracy with fewer selected features and lower computational cost compared to other methods.
APPLIED INTELLIGENCE
(2023)
Article
Mathematics, Interdisciplinary Applications
Xiaoli Gao, Yangfei Yuan, Jie Li, Weifeng Gao
Summary: This paper proposes a hybrid model based on decomposition for solving constrained optimization problems. By transforming the problem into a biobjective optimization problem and dividing it into subproblems, which are optimized using direction vectors, the method gradually approaches the global optimal solution.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2022)
Article
Multidisciplinary Sciences
Suhaila Abd Halim, Yupiter H. P. Manurung, Muhamad Aiman Raziq, Cheng Yee Low, Muhammad Saufy Rohmad, John R. C. Dizon, Vladimir S. Kachinskyi
Summary: In this study, an application tool was developed using open-sourced and customized algorithm based on artificial neural networks to optimize resistance spot welding. The tool can predict the effects of welding parameters on tensile shear load bearing capacity and weld quality classifications with high accuracy. It provides a cost-effective and practical solution for small industries and research centers.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Interdisciplinary Applications
Juniqi Liu, Zeqiang Zhang, Juhua Gong, Feng Chen, Tao Yin, Yu Zhang
Summary: This paper proposes a corridor allocation problem that allows irregular logistics material handling positions. A clonal selection algorithm is used to optimize the material handling layout, and the performance of the algorithm is verified through benchmark instances and an actual case study.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Naili Luo, Yulong Ye, Wu Lin, Qiuzhen Lin, Victor C. M. Leung
Summary: A novel multimodal multiobjective memetic algorithm is proposed in this paper, which preserves more global and local Pareto optimal solution sets using a local detection mechanism and a clustering-based selection strategy. Experimental results demonstrate the superior performance of the proposed algorithm.
Article
Computer Science, Artificial Intelligence
Lingjie Li, Qiuzhen Lin, Zhong Ming
Summary: This paper presents a comprehensive survey on the multi-objective immune algorithm (MOIA), which is a heuristic algorithm based on the artificial immune system model. MOIAs can be classified into three main categories: multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. The characteristics, principles, theoretical analyses, and performance of MOIAs in solving various types of MOPs are discussed. The paper concludes with a brief summary of the current drawbacks, challenges, and future directions for MOIAs.
Review
Computer Science, Artificial Intelligence
Lijia Ma, Zengyang Shao, Lingling Li, Jiaxiang Huang, Shiqiang Wang, Qiuzhen Lin, Jianqiang Li, Maoguo Gong, Asoke K. Nandi
Summary: In this paper, a comprehensive review of heuristic and metaheuristic biological network alignment methods is presented. Comparative analyses of alignment models, datasets, evaluation metrics, and experimental results are provided, along with conclusions and possible future directions for BNAs.
Article
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes a fuzzy decomposition-based MOEA that estimates the population's shape using fuzzy prediction and selects weight vectors to fit the Pareto front shapes of different multi-objective optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yuchao Su, Naili Luo, Qiuzhen Lin, Xia Li
Summary: Multiobjective optimization is crucial in practical engineering applications, but becomes more challenging with increased number of objectives. This paper proposes a many-objective immune algorithm that utilizes global information to select high-quality parents for evolution, enhancing convergence and diversity of the population.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Xunfeng Wu, Lijia Ma, Jianqiang Li, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an ensemble surrogate-based framework for solving computationally expensive multiobjective optimization problems (EMOPs). The framework trains a global surrogate model and multiple surrogate submodels to enhance prediction accuracy and reliability. Experimental results demonstrate the advantages of this approach in solving EMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zengyang Shao, Lijia Ma, Yuan Bai, Shanfeng Wang, Qiuzhen Lin, Jianqiang Li
Summary: This paper proposes a decomposition based multiobjective memetic algorithm for multiresolution community detection in complex networks. The method models the problem as a multiobjective optimization problem and combines evolutionary algorithm with local search to detect communities at multiple resolution levels. Experimental results demonstrate the effectiveness of the method.
Article
Computer Science, Artificial Intelligence
Yongfeng Li, Lingjie Li, Qiuzhen Lin, Ka-Chun Wong, Zhong Ming, Carlos A. Coello
Summary: This paper proposes a self-organizing weighted optimization based framework (S-WOF) for solving large-scale multi-objective optimization problems (LSMOPs). The S-WOF achieves a dynamic trade-off between convergence and diversity by adjusting the weights and evaluation numbers. In addition, an efficient competitive swarm optimizer (CSO) is implemented to improve the search ability. Experimental results demonstrate the superiority of S-WOF over several state-of-the-art large-scale evolutionary algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Lingjie Li, Yongfeng Li, Qiuzhen Lin, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a convergence and diversity guided leader selection strategy (CDLS) for improving the performance of particle swarm optimizer (PSO) in high-dimensional objective space. By adaptively selecting different leader particles based on each particle's situation, CDLS achieves a good tradeoff between convergence and diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Junchuang Cai, Qingling Zhu, Qiuzhen Lin
Summary: This paper introduces a new Dynamic Pickup and Delivery Problem (DPDP) and proposes an algorithm called VNSME to solve it. The algorithm performs well in practical scenarios and achieves the first place in the ICAPS 2021 competition.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Jia Wang, Chengyu Wang, Qiuzhen Lin, Chengwen Luo, Chao Wu, Jianqiang Li
Summary: This paper provides a comprehensive survey of recent advances in adversarial attack and defense methods. It analyzes and compares the pros and cons of various schemes, and discusses the main challenges and future research directions in this field.
Article
Automation & Control Systems
Xunfeng Wu, Qiuzhen Lin, Jianqiang Li, Kay Chen Tan, Victor C. M. Leung
Summary: In this article, an ensemble surrogate-based coevolutionary optimizer is proposed to solve large-scale optimization problems. By training local surrogate models and using feature selection to construct a selective ensemble surrogate, the optimizer approximates the target problem. With two populations solving the target problem and a simplified auxiliary problem collaboratively, the coevolutionary optimizer can leverage the search experience from the auxiliary problem to help solve the target problem.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Junkai Ji, Jiajun Zhao, Qiuzhen Lin, Kay Chen Tan
Summary: The dendritic neural model (DNM) is a computationally fast machine-learning technique that can be implemented using logic circuits and binary calculations. In order to enhance its speed, a more concise architecture can be generated. However, existing multiobjective evolutionary algorithms face limitations in solving this large-scale multiobjective optimization problem. Therefore, a novel competitive decomposition-based algorithm is proposed in this study, which outperforms state-of-the-art algorithms in terms of optimization ability. Experimental results also demonstrate that the proposed algorithm can achieve competitive performance when applied to DNM and its hardware implementation, compared to widely used machine-learning approaches.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Yuchao Su, Qiuzhen Lin, Zhong Ming, Kay Chen Tan
Summary: This article proposes an effective method called Adapted Decomposed Directions (ADDs) for solving Multiobjective Optimization Problems (MOPs). Instead of using a single ideal or nadir point, each weight vector has its own ideal point for decomposition, and the decomposed directions are adaptively adjusted during the search process. The experimental results show that this method significantly improves the performance of three representative Multiobjective Evolutionary Algorithms (MOEAs) and outperforms seven competitive MOEAs in solving various artificial MOPs and a real-world MOP.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Lijia Ma, Zengyang Shao, Xiaocong Li, Qiuzhen Lin, Jianqiang Li, Victor C. M. Leung, Asoke K. Nandi
Summary: This article proposes an evolutionary deep reinforcement learning algorithm called EDRL-IM for influence maximization in complex networks. By combining evolutionary algorithm and deep reinforcement learning algorithm, EDRL-IM outperforms state-of-the-art methods in finding seed nodes.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
