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Pareto optimal allocation of fault current limiter based on immune algorithm considering cost and mitigation effect

Journal of Modern Power Systems and Clean Energy 2017年第5期5卷 820-829页

作者： Baichao CHEN Liangliang WEI Yuanzhe ZHU Yongheng ZHONG Jiaxin YUAN Yang LEI School of Electrical Engineering Wuhan UniversityWuhan 430072China

This paper presents a multi-objective Pareto optimal method for allocation of fault current limiters based on an immune algorithm, which takes into account two objectives of the cost and fault current mitigation effect. A sensitivity factor calculation method based on the rate of fault current mitigation is proposed to reduce the search space and improve the efficiency of the *** this approach, the objective functions related to the cost and fault current mitigation effect are established. A modified inversion operator based on equal cost is proposed to converge to global optimal solutions more effectively. The proposed algorithm is tested on the IEEE39-bus system, and obtains the Pareto optimal solutions,from which the user can select the most suitable solutions according to the preferences and relative importance of the objective functions. Simulation results are used to verify the proposed method.

关键词： FCL optimal allocation Fault current mitigation effect Modified inversion operator Pareto optimal solutions immune algorithm

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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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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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An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem

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ENGINEERING OPTIMIZATION 2014年第9期46卷 1269-1283页

作者： Xu, Ye Wang, Ling Wang, Shengyao Liu, Min Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.

关键词： immune algorithm distributed permutation flow-shop scheduling decoding method local search

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A new design optimization framework based on immune algorithm and Taguchi's method

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COMPUTERS IN INDUSTRY 2009年第8期60卷 613-620页

作者： Yildiz, Ali Riza Uludag Univ Dept Mech Engn TR-16059 Bursa Turkey

This paper describes an innovative optimization approach that offers significant improvements in performance over existing methods to solve shape optimization problems. The new approach is based on two-stages which are (1) Taguchi's robust design approach to find appropriate interval levels of design parameters (2) immune algorithm to generate optimal solutions using refined intervals from the previous stage. A benchmark test problem is first used to illustrate the effectiveness and efficiency of the approach. Finally, it is applied to the shape design optimization of a vehicle component to illustrate how the present approach can be applied for solving shape design optimization problems. The results show that the proposed approach not only can find optimal but also can obtain both better and more robust results than the existing algorithm reported recently in the literature. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Shape optimization immune algorithm Taguchi's method Robust design

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Self-adaptive learning based immune algorithm

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Journal of Central South University 2012年第4期19卷 1021-1031页

作者：许斌庄毅薛羽王洲 Department of Computer Engineering&Science Nanjing University of Aeronautics&Astronautics Science and Technology on Electron-optic Control Laboratory

A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.

关键词： immune algorithm multi-modal optimization evolutionary computation immtme secondary response self-adaptivelearning

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Amplitude-only pattern nulling of linear antenna arrays with the use of an immune algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2008年第5期18卷 397-409页

作者： Guney, Kerim Babayigit, Bilal Erciyes Univ Dept Elect & Elect Engn Fac Engn TR-38039 Kayseri Turkey Erciyes Univ Dept Comp Engn Fac Engn TR-38039 Kayseri Turkey

This article presents an optimization immune algorithm (opt-IA) for null steering of linear antenna arrays by controlling only the element amplitudes. Nulling of the pattern is also achieved by controlling the phase-only and the complex weights (both the amplitude and phase) of the array elements. The opt-IA is a new evolutionary computing algorithm based on the clonal selection principle of immune system. To show the accuracy and flexibility of the proposed opt-IA, several examples of Chebyshev array pattern with the imposed single, multiple, and broad nulls are given. It is found that the nulling technique based on opt-IA is capable of steering the array nulls precisely to the undesired interference directions. (c) 2008 Wiley Periodicals, Inc.

关键词： antenna array immune algorithm pattern nulling pattern synthesis

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An immune algorithm for the integration of process planning and scheduling

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2008年第1-2期33卷 95-108页

作者： Tian, Ying Jiang, Pingyu Zheng, Mei Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China

Process planning may generate several feasible process plans for each job in flexible manufacturing environment. To get the global optimality, it is necessary to integrate the process planning with scheduling. An integration model for process planning and scheduling is proposed in this paper. On the basis of natural immune theory, an immune algorithm (IA) is presented to solve the integration problem. The algorithm is based on clonal selection and affinity maturation for finding optimal solutions. To demonstrate the efficiency of the IA, some numerical experiments are carried out and analysed. The experiment results show that the process planning and scheduling problems may be effectively solved simultaneously using the proposed IA approach.

关键词： immune algorithm IA process planning scheduling

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A multi-objective optimization model based on immune algorithm in wireless mesh networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2016年第1期29卷 155-169页

作者： Chen, Jing He, Kun Du, Ruiying Yu, Fajiang Yuan, Quan Wang, Lina Fu, Cai Wuhan Univ Sch Comp Wuhan 430072 Hunan Peoples R China Univ Texas Permian Basin Dept Math & Comp Sci Odessa TX USA Huazhong Univ Sci & Technol Comp Dept Wuhan 430074 Peoples R China

With the characteristics of high self-organized, dynamic, and interoperable, the wireless mesh network (WMN) is deemed as a potential technology to be applied widely for home, enterprise, and social public service. Many current optimization schemes usually focus on a single metric such as network deployment cost, throughput, QoS, and so on, but few schemes consider that the optimized metric may affect other metrics of WMN. In practice, the influence among the different metrics is often nonignorable. To optimize the performance from a global perspective, we propose a multi-objective optimization model based on immune algorithm (MOM-IA), which provides a paradigm to find the optimal solution satisfying some different restriction conditions. To simplify, MOM-IA mainly analyzes the restriction relationship of connectivity, redundancy, and throughput, which are the multiple objects. Considering the characteristic of dynamic and the discrete integer parameters in WMN, we design a longtime evolution immune algorithm to solve the MOM. Finally, the analysis of experiments presents that MOM-IA has good performance in solution set diversity and Pareto-front distribution, which means the probability to find the optimal solution in WMN. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： WMNs multi-objective optimization model immune algorithm

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