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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： H.S.LIN ZHUO-YUE SONG XIAN-LIN HUANG X.Z.GAO Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China Institute of Intelligent Power Electronics Helsinki University of Technology Espoo Finland

This paper proposes a modified immune optimization algoritom for the multi-modal problems. It is inspired by the clonal selection and antibody diversity maintaining principles in the immunology. Compared with the existing immune optimization algorithms, our new algorithm defines the concentration of the antibody to stand for the solution diversity, and the mutation rate is dynamically adjusted based on the antibody concentration and fitness. Moreover, this algorithm takes advantage of the clusters of antibodies. For each cluster,the elitist ones are always retained as the memory set. Therefore, an improved trade-off between the exploration and exploitation in the solution space can be achieved. A few multi-modal and high-dimension benchmark functions are utilized here to examine the efficiency of our optimization method. Performance comparisons are also made with the CLONALG and opt-aiNET. Simulation results demonstrate that this modified optimization algorithm can effectively ob tain the optimal solutions, and still maintain the solution diversity, which is crucial for dealing with challenging real-world optimization problems.

关键词： immune optimization algorithm Diversity Cional selection Multi-modal optimization Mutation rate

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Constrained multiobjective optimization immune algorithm: Convergence and application

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2006年第5期52卷 791-808页

作者： Zhang, Zhuhong Univ Guizhou Dept Math Guiyang 550025 Guizhou Peoples R China

A new optimization technique, multiobjective optimization immune algorithm for constrained nonlinear multiobjective optimization problems is designed based on immune metaphors of humoral immune and Pareto optimality, especially, some interactive metaphors between antigen population and antibody population. It includes four main mechanisms: (1) constraint-handling operation that provides an alternative feasible solution set for rapidly finding Pareto optimal solutions;(2) antibody evolution associated with clonal selection principle and ideas of immune regulation;(3) competition and update of antigens that induces evolution of antibody populations;(4) memory pool used for collecting the best solutions of evolving antibody populations. Convergence is proven through Markov theory as well as demonstrated by the experiment results. Comparative analysis and applications illustrate that it is effective and valuable. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： constrained multiobjective optimization humoral immune immune optimization algorithm Pareto optimality convergence

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An improved reinforcement-immune algorithm for agricultural resource allocation optimization

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Journal of Computational Science 2018年 27卷 320-328页

作者： Jiang, Yimin Hao, Kuangrong Cai, Xin Ding, Yongsheng Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai 201620 China

Due to the decreasing and limited agricultural resources, developing techniques for agricultural resource allocation optimization has received increasing attention in recent years. In this paper, an improved reinforcement-immune algorithm (RIA) is proposed to realize an efficient and intelligent optimization in agricultural resource allocation and routing problem of the delivery vehicles. By combining the strong self-adaptability and goal-driven performance of reinforcement learning with the antibody diversity and strong global search capability of immune optimization, RIA has better performance not only on the convergence property but also on the effectiveness of finding the optimal solution. First, we introduce Table Q as the action policy. Each element in Table Q means the action score that we choose delivery vehicle v to allocate resource t of supply center s to field i. Table Q is initialized according to the distance between the field and the supply center. Second, we update Table Q to learn good genetic information according to the optimal antibody after each iteration. Each antibody represents an allocation scheme. Third, at the stage of antibody mutation, we use Table Q to guide antibodies, which makes the algorithm adaptive and goal-driven. Finally, simulation results illustrate that the proposed algorithm is effective in improving the climbing performance, searching ability and finding the optimal solution. © 2018 Elsevier B.V.

关键词： immune optimization algorithm Reinforcement learning Reinforcement-immune algorithm Resource allocation

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An immune algorithm for solving the optimization problem of locating the battery swapping stations

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KNOWLEDGE-BASED SYSTEMS 2022年 248卷

作者： Zhang, Huizhen Zhang, Kun Zhou, Yuyang Ma, Liang Zhang, Ziying Univ Shanghai Sci & Technol Sch Management Mail Box 459516 Jungong Rd Shanghai 200093 Peoples R China Shanghai Univ Engn Sci Sch Mat Engn 333 Longteng Rd Shanghai 201620 Peoples R China

Compared with fuel-powered vehicles, new energy electric vehicles have less carbon emissions. The application of new energy electric vehicles in logistics transportation is very significant to reduce vehicle-emitted greenhouse gases and improve environmental protection. In the logistics network using electric transport vehicles, the reasonable layout of battery swapping stations not only improves the distribution efficiency but also saves the distribution cost. In this paper, an integer programming problem model was formulated for reasonably locating battery swapping stations, where the total costs (including construction costs, operation and maintenance costs, and travel costs) were minimized. To solve this problem, we proposed a solution strategy based on an immune optimization algorithm, two crossover operators, and variable crossover and mutation rates. The effectiveness of the presented model and the performance of the proposed solution approach were evaluated using a case study. Compared with the commercial software CPLEX, the numerical results showed that the proposed algorithm can provide the optimal solution with less computing time. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Location problem Battery swapping station immune optimization algorithm Variable crossover and mutation rates

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Multidisciplinary design optimization for air-condition production system based on multi-agent technique

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Journal of Central South University 2012年第2期19卷 527-536页

作者：杨海东鄂加强屈挺 Department of Mechatronics Engineering Guangdong University of Technology College of Mechanical and Vehicle Engineering Hunan University

In order to guarantee the overall production performance of the multiple departments in an air-condition production industry, multidisciplinary design optimization model for production system is established based on the multi-agent technology. Local operation models for departments of plan, marketing, sales, purchasing, as well as production and warehouse are formulated into individual agents, and their respective local objectives are collectively formulated into a multi-objective optimization problem. Considering the coupling effects among the correlated agents, the optimization process is carried out based on self-adaptive chaos immune optimization algorithm with mutative scale. The numerical results indicate that the proposed multi-agent optimization model truly reflects the actual situations of the air-condition production system. The proposed multi-agent based multidisciplinary design optimization method can help companies enhance their income ratio and profit by about 33% and 36%, respectively, and reduce the total cost by about 1.8%.

关键词： multi-agent system production operation multidisciplinary optimization self-adaptive chaos optimization immune optimization algorithm

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Structural optimization of Ag-Pd clusters based on different potential parameterizations

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CHEMICAL PHYSICS 2011年第1期390卷 36-41页

作者： Wu, Xia Wu, Yiping Kai, Xiaoming Wu, Genhua Chen, Youcun Anqing Normal Univ Sch Chem & Chem Engn Anhui Prov Lab Optoelect & Magnetism Funct Mat Anqing 246011 Peoples R China

The putative stable structures of bimetallic Ag-Pd clusters are investigated. Gupta potential is applied to describe the interatomic interactions in Ag-Pd clusters. Experimental-fitted parameters and density-functional-theory (DFT)-fitted parameters are used to determine the lowest energy structures. Global optimization of AgmPdn (m + n = 15) and Ag3mPd38 (3m) (m = 1-12) clusters is performed using adaptive immune optimization algorithm (AIOA). The growth rules of Ag-Pd clusters for both sets of parameterizations are studied, and the differences of structures and excess energies are compared. With the order parameters adopted to show the atomic distribution in the clusters, it is shown that for both parameterized clusters silver atoms have strong tendencies towards segregating at the surface of the structures. However, for both potentials, the atomic distribution of Ag and Pd atoms in Ag-Pd clusters is different because of the geometrical and symmetrical difference. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Ag-Pd clusters Structural optimization immune optimization algorithm Gupta potential

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Research on subaperture stitching optimization algorithm for large flat mirror testing 9

Research on subaperture stitching optimization algorithm for...

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Conference on Optical Metrology and Inspection for Industrial Applications IX Part of SPIE/COS Photonics Asia Conference

作者： Zhou, Zhixuan Han, Sen Xu, Chunfeng Wang, Quanquan Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

ISBN: (纸本)9781510657052;9781510657045

To address the problem of high-precision detection of large flat aperture mirror shapes, the current subaperture stitching methods are analyzed. The results show that the full aperture surface shape stitching errors due to subaperture adjustment errors and localization errors are the main reasons affecting the high precision detection. To improve the detection accuracy of subaperture stitching, we propose an immune optimization algorithm for subaperture stitching. The algorithm reduces the influence of subaperture adjustment errors and positioning errors, so that the PV value of reconstructed shape reaches lambda/100. It is shown that the proposed method can effectively control the stitching errors and improve the stitching accuracy.

关键词： flat mirror testing subaperture stitching stitching error immune optimization algorithm

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Multi-objective immune algorithm with Baldwinian learning

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APPLIED SOFT COMPUTING 2012年第8期12卷 2654-2674页

作者： Qi, Yutao Liu, Fang Liu, Meiyun Gong, Maoguo Jiao, Licheng Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators designed for SOPs might not be suitable for MOPs due to the different optima structures between them. At present, few works have been done to improve the searching efficiency of MOEAs according to the characteristic of MOPs. Based on the regularity of continues MOPs, a Baldwinian learning strategy is designed for improving the nondominated neighbor immune algorithm and a multi-objective immune algorithm with Baldwinian learning (MIAB) is proposed in this study. The Baldwinian learning strategy extracts the evolving environment of current population by building a probability distribution model and generates a predictive improving direction by combining the environment information and the evolving history of the parent individual. Experimental results based on ten representative benchmark problems indicate that, MIAB outperforms the original immune algorithm, it performs better or similarly the other two outstanding approached NSGAII and MOEA/D in solution quality on most of the eight testing MOPs. The efficiency of the proposed Baldwinian learning strategy has also been experimentally investigated in this work. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Multi-objective optimization problems immune optimization algorithm Memetic algorithm Baldwinian learning

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Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第3期8卷 2433-2445页

作者： Chen, Kesheng Luo, Wenjian Lin, Xin Song, Zhen Chang, Yatong Harbin Inst Technol Sch Comp Sci & Technol Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Sch Future Technol Nanjing 210044 Peoples R China

Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.

关键词： Autonomous aerial vehicles Safety optimization Path planning Pareto optimization Energy consumption Pollution Biparty multiobjective optimization UAV path planning immune optimization algorithm evolutionary algorithm

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Fuzzy Modeling with immune algorithm to Conceptual Design of Mechanisms

Fuzzy Modeling with Immune Algorithm to Conceptual Design of...

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Cai Chi-lan Bai Yue-wei He Ya-Fei Zhang Zhi-ming Shanghai Second Polytech Univ Sch Mech & Elect Engn Shanghai Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9781424442461

This paper presents a fuzzy modeling with immune algorithm to search for the optimal solution in conceptual design of mechanisms. First, a new problem formulation model that involves fuzzy logic to reflect the imprecise nature of objectives is presented. Then, an immune optimization algorithm (IOA) is used as a key technology to find the optimal solution in many combinatorial solutions, which can avoid combinatorial explosion effectively. The fuzzy logic integrating expert knowledge can be effectively utilized in calculating fitness in IOA-based combinatorial optimization to deal with imprecise information. Finally, a design case is presented.

关键词： conceptual design fuzzy logic fuzzy modeling immune optimization algorithm

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