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5th IEEE International Conference on Cognitive Informatics (ICCI 2006)

作者： Xu, Lifang Mo, Hongwei Wang, Kejun Harbin Engn Univ Automat Coll Harbin Peoples R China

ISBN: (纸本)9781424404759

This paper centers on a novel data mining technique we term immune supervised clustering. Unlike traditional clustering, immune supervised clustering assumes that the examples are classified by immune algorithm. The goal of immune supervised clustering algorithm(ISCA) is to identify class-uniform clusters that have high probability densities. The experimental results suggest that ISCA, although runtime intensive, finds the best clusters in almost all experiments conducted.

关键词： supervised clustering immune algorithm clustering for classification

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immune algorithm-based Transportation Planning

Immune Algorithm-based Transportation Planning

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Zhang, Li-Ping Yang, Zong-Xiao Li, Gen-Sheng Henan Univ Sci & Technol Henan Engn Lab Wind Power Syst Inst Syst Sci & Engn 48 Xiyuan Rd Luoyang 471003 Peoples R China

ISBN: (纸本)9781509053469

The industrial structure of the cities has undergone great changes, while the population is also in a large number of flows, which undoubtedly makes Chinese urban transportation construction and management face new standards and challenges. We proposed using immune algorithm to solve the transportation planning problems in this paper to shorten the total length of transmission network and transportation network, and developed a MATLAB software with the immune algorithm procedures to solving the engineering problems. By running MATLAB program got 5 substation construction points and 2 urban rail construction centers, the results showed that it can shorten the path and reduce the cost of investment to a certain extent, and the immune algorithm has global optimization, parallel search, high efficiency and other characteristics. Through the verification of the northeast transmission network optimization analysis and the central plains urban agglomeration intercity rail transportation network planning optimization analysis, demonstrated the feasibility and effectiveness of the immune algorithm.

关键词： immune algorithm Transportation planning Optimization analysis Mathematical model Case study

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An efficient quantum immune algorithm to minimize mean flow time for hybrid flow shop problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2012年 84卷 1-25页

作者： Niu, Qun Zhou, Taijin Fei, Minrui Wang, Bing Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

Hybrid flow shop problem (HFSP) can be regarded as a generalized flow shop with multiple processing stages, of which at least one consists of parallel machines. HFSP is fairly common in flexible manufacturing and in process industry. This paper presents an efficient quantum immune algorithm (QIA) for HFSP. The objective is to find an optimal job sequence that minimize the mean flow time. Since HFSP has been proved to be NP-hard in a strong sense even in case of two stages. immune algorithm (IA) and quantum algorithm (QA) are used to solve the problem, respectively. To improve the performance of IA, an effective IA with new adaptive crossover and fractional parts mutation operators is proposed. which is called AIA. A randomly replacing strategy is employed to promote population diversity of QA. namely RRQA. In order to achieve better results, the paper proposes a quantum immune algorithm (QIA). which combines IA with QA to optimize the HFSP. Furthermore, all the improvements are added into QIA to be ARRQIA, which possesses the merits of global exploration, fast convergence, and robustness. The simulation results show that the proposed AIA significantly enhances the performance of IA. RRQA also produces more efficient and more stable results than QA. So far as ARRQIA is concerned, it outperforms the other algorithms in the paper and the average solution quality has increased by 3.37% and 6.82% compared with IA and QA on the total 60 instances. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Hybrid flow shop scheduling immune algorithm Quantum algorithm Mean flow time

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Scheduling of cogeneration plants considering electricity wheeling using enhanced immune algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2005年第1期27卷 31-38页

作者： Chen, SL Tsay, MT Gow, HJ Cheng Shiu Univ Dept Elect Engn Kaohsiung 833 Taiwan

A new method based on immune algorithm (IA) is presented to solve the scheduling of cogeneration plants in a deregulated market. The objective function includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) is proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Table lists with heuristic rules are also employed in the searching process to enhance the performance. EIA is also compared with the original IA. Test results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： immune algorithm cogeneration plants deregulated market

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Learning-Inspired immune algorithm for Multiobjective-Optimized Multirobot Maritime Patrolling

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 9870-9881页

作者： Huang, Li Zhou, Mengchu Han, Hua Wang, Shouguang Albeshri, Aiiad Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Zhejiang Gongshang Univ Coll Informat & Elect Engn Hangzhou Peoples R China King Abdulaziz Univ Dept Comp Sci Jeddah 21481 Saudi Arabia

Multirobot patrolling systems with various sensing and communications devices are deployed to guarantee maritime safety. Patrolling path planning for multiple robots can be modeled as a multiobjective optimization problem. The positions of patrolling nodes impact the length of patrolling paths and execution efficiency of robots. To compute them, a huge solution space is encountered. Besides, multiple patrolling nodes on the same line lead to the same patrolling scheme. Thus, how to promote solution (population) diversity becomes a new challenge. To tackle it, this work proposes a learning-inspired immune algorithm. It uses the historical information in the previous generations during iterations to realize a learning process. Unlike saving all the individuals themselves and training a model for them, the useful historical information is extracted by using upper confidence bound-based and actor-critic-inspired methods. Both time consumption and storage space can be dramatically saved. The experimental results indicate that the proposed algorithm can generate multiple patrolling schemes for the decision makers and outperforms the state-of-the-art.

关键词： Actor-critic immune algorithm multiobjective optimization multirobot maritime patrolling upper confidence bound (UCB)

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Cigarette Production Scheduling by Combining Workflow Model and immune algorithm

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第1期11卷 251-264页

作者： Zuo, Xingquan Tan, Wei Lin, Huiping Beijing Univ Posts & Telecommun Comp Sch Beijing 100876 Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing 100876 Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Peking Univ Sch Software & Microelect Beijing 100084 Peoples R China

Cigarette production scheduling is vital in improving production efficiency and reducing supply delay. In this paper, a workflow model combined with an immune algorithm is proposed to solve this problem for improving production efficiency. First, the problem is formulated as a mixed-integer quadratically constrained programming model. Afterwards, the problem is transformed into a workflow resource allocation one. Based on this model, an immune algorithm is presented to find a set of activity priorities that are combined with dispatching rules to allocate resources. Activity priorities are represented by antibodies and evaluated by simulation runs on the workflow model. The proposed approach is applied to several production scheduling instances, and results are compared with other approaches. Experiments show that the result from the proposed approach is substantially better than those obtained from other approaches. It is demonstrated that our approach can effectively reduce production tardiness and improve efficiency. Note to Practitioners-The key issue of the cigarette production scheduling problem is to effectively schedule the primary processing line, cut-tobacco silos, and cigarette making machines. In many tobacco companies, these steps are done manually based on experiences. In this paper, we propose a systematic method to schedule these components by combining a workflow model and an immune algorithm. The model and algorithm are used to model cigarette production process and find a good scheduling solution, respectively. The advantage of such model is that it has an intuitive structure that is easily used by industrial practitioners. It is also convenient to use the model to describe more complex problems because of its description capability. The algorithm can find activity priorities quickly, and then dispatching rules combined with these priorities are used to schedule resources in a real-time manner. This approach can greatly improve the quality of sched

关键词： Artificial immune system cigarette production scheduling immune algorithm workflow model

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A heuristically directed immune algorithm to minimize makespan and total flow time in permutation flow shops

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第9-12期93卷 3759-3776页

作者： Chakravorty, Arindam Laha, Dipak Jadavpur Univ Dept Mech Engn Kolkata 700032 India

As an emerging novel evolutionary technique, the immune algorithm has gained a lot of attention and wide applications in various fields of engineering and management. To our knowledge, this paper first considers the application of immune algorithm for the classic permutation flow shop scheduling problem. We present a hybrid heuristic combining immune algorithm and simulated annealing for the n-job, m-machine permutation flow shop scheduling problem to minimize makespan and total flow time. A heuristically directed population-based construction approach in the immune algorithm and a forward/backward shift neighborhood search heuristic in the simulated annealing of the proposed method is utilized to explore the search process for better schedules of jobs as well to speed up the convergence speed of the proposed algorithm. The proposed method is tested with Taillard's flow shop scheduling benchmark instances for different problems with job sizes varying from 20 to 500. The computational results demonstrate that the proposed heuristic is very competitive with the state-of-the-art procedures in terms of both solution quality and computational times.

关键词： Scheduling Permutation flow shops Makespan Total flow time Heuristically directed population-based approach immune algorithm Simulated annealing Optimization

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Fuzzy least squares support vector machine soft measurement model based on adaptive mutative scale chaos immune algorithm

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Journal of Central South University 2014年第2期21卷 593-599页

作者：王涛生左红艳 Research Center of Engineering Technology for Engineering Vehicle Chassis Manufacturing of Hunan Province Research Base of Multinational Investment and Operations in Hunan Province School of Resource and Safety Engineering Central South University

In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine(FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune *** simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and *** application results show that the relative errors of the soft measurement model are less than 3.34%.

关键词： chaos immune algorithm fuzzy support vector machine

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Structural design of the danger model immune algorithm

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INFORMATION SCIENCES 2012年 205卷 20-37页

作者： Xu, Qingyang Wang, Song Zhang, Caixia Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Shandong Peoples R China

The traditional immune algorithm (IA) is based on a self-nonself biological immunity mechanism. Recently, a novel immune theory called the danger model theory has provided more suitable biological information for data handling compared with the self-nonself mechanism. According to the danger model theory and based on past experiences of the genetic and artificial IA, we present the Danger Model immune algorithm (DMIA) that differs from the traditional IA in terms of the self-nonself biological immunity mechanism. We define a danger area and a danger signal in DMIA. We use the selection, mutation, and specific danger operators to update the population. The algorithm can achieve complex problem optimization. Simulation studies demonstrate that DMIA exhibits a higher efficiency than traditional genetic algorithms and other algorithms when considering a number of complicated functions. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Danger model immune algorithm Danger signal Danger area

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Optimisation of inverter placement for mass rapid transit systems by immune algorithm

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IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS 2005年第1期152卷 61-71页

作者： Chuang, HJ Kao Yuan Inst Technol Dept Elect Engn Lu Chu Taiwan

Optimal inverter substation planning is solved by minimising the overall cost of power consumption and inverter investment for mass rapid transit power systems with immune algorithm (IA). The objective function and constraints are expressed as antigens, and all feasible solutions are expressed as antibodies in the IA simulation process. The diversity of antibodies is then enhanced by considering the proximity of antigens so that the global optimisation during the solution process can be obtained. It is found that energy regeneration, which results from the braking operation of train sets approaching the next station, can be restored effectively by the optimal planning of inverters using the proposed immune algorithm.

关键词： optimisation energy regeneration antibodies diversity mass rapid transit power system inverter placement Optimisation techniques Power convertors and power supplies to apparatus regenerative braking objective function overall cost minimisation immune algorithm optimal inverter substation planning rapid transit systems power consumption inverter investment substations IA simulation process railway electrification train set braking operation cost reduction IA investment Transportation invertors Substations objective constraint

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