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An improved clonal selection algorithm and its application to traveling salesman problems

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2007年第12期E90A卷 2930-2938页

作者： Gao, Shangce Tang, Zheng Dai, Hongwei Zhang, Jianchen Toyama Univ Fac Engn Toyama 9308555 Japan Tele Elect Supply Serv Co Ltd Takaoka Toyama 9391119 Japan

The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.

关键词： clonal selection algorithm distance-based somatic hypermutation traveling salesman problem chaotic affinity

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A Novel clonal selection algorithm for Community Detection in Complex Networks

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COMPUTATIONAL INTELLIGENCE 2015年第3期31卷 442-464页

作者： Cai, Qing Gong, Maoguo Ma, Lijia Jiao, Licheng Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Recent years have seen the arising recognition of community detection in complex networks. Artificial immune systems, owing to their inherent properties, have been thoroughly studied and well applied to practical use. In this article, one of the well-known artificial immune system models, named clonal selection algorithm, is introduced to reveal community structures in complex networks. By introducing a novel antibody population initialization mechanism and a novel hypermutation strategy, the proposed approach could be applied to moderate-scale network. Besides, by optimizing an objective function called modularity density, the proposed algorithm is also capable of detecting community structure at multiple resolution levels. Experiments on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.

关键词： community detection artificial immune system clonal selection algorithm complex network modularity density

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Synthesis of Low Sensitivity Broadband Matching Networks Using clonal selection algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2016年第7期14卷 3168-3177页

作者： Dornelles, L. T. Sa, L. B. D. Mesquita Filho, A. C. D. IME Rio De Janeiro Brazil Ctr Tecnol Exercito CTEx Rio De Janeiro Brazil Univ Fed Rio de Janeiro UFRJ Programa Engn Eletr Rio De Janeiro Brazil

This paper presents a new methodology for synthesis of broadband matching networks based on clonal selection algorithms (CSA). This metaheuristic uses the hypermutation as only variation operator resulting in a gradual evolving of the network topology. A closed form expression for the transducer power gain (TPG) sensitivity with respect to the component values is employed, in such a way that the effects of the components tolerance on the matching network performance can easily be quantified. The evolvable function proposed is single-objective and eliminates the difficult task of assigning appropriate weights to parameters. The evaluation of the TPG sensitivity enables the designer to identify and remove irrelevant components of the circuit, simplifying it. The efficiency of the methodology is tested in two cases found in the literature: the traditional project of impedance matching for a simple RLC load proposed by Fano [1];and a real synthesis of impedance matching network for a monopole whip antenna, proposed in [2]. The results are compared with other existing methods.

关键词： Broadband Impedance Matching clonal selection algorithm Low Sensitivity

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Solving the task assignment problem with ant colony optimisation incorporating ideas from the clonal selection algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第2期7卷 129-143页

作者： Martinovic, Goran Bajer, Drazen Josip Juraj Strossmayer Univ Osijek Fac Elect Engn Osijek 31000 Croatia

The task assignment problem commonly appears in distributed computing environments. It asks an assignment of tasks to processors is found such that it satisfies the imposed constraints and that the total execution and communication cost of the tasks is minimal. This paper presents an algorithm based on ant colony optimisation that incorporates ideas from the clonal selection algorithm. Namely, the ant colony optimisation algorithm includes the cloning of the iteration-best ant and mutation of its clones' solutions;the goal being a better exploitation of promising parts of the search space. Besides that, the solution construction procedure is modified to take the memory constraints into account and the pheromone update mechanism is modified to enable the best clone to deposit pheromone. The experimental analysis, conducted on a large number of problem instances, showed that the proposed algorithm performs better compared to the MAX-MIN ant system, a differential evolution and a particle swarm optimisation algorithm.

关键词： ant colony optimisation ACO clonal selection algorithm CSA clones' solutions cloning differential evolution DE iteration-best ant MAX-MIN ant system MMAS memory constraints mutation particle swarm optimisation PSO pheromone update mechanism solution construction procedure task assignment problem TAP

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Quantum Interference Crossover-Based clonal selection algorithm and Its Application to Traveling Salesman Problem

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2009年第1期E92D卷 78-85页

作者： Dai, Hongwei Yang, Yu Li, Cunhua Shi, Jun Gao, Shangce Tang, Zheng Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Peoples R China Toyama Univ Fac Engn Toyama 9308555 Japan

clonal selection algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based clonal selection algorithm.

关键词： clonal selection algorithm quantum interference crossover traveling salesman problem hybrid model

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Hybrid learning clonal selection algorithm

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INFORMATION SCIENCES 2015年第1期296卷 128-146页

作者： Peng, Yong Lu, Bao-Liang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Ctr Brain Like Comp & Machine Intelligence Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Educ Commiss Intelligent Interact & Cogn Key Lab Shanghai 200240 Peoples R China

Artificial immune system is a class of computational intelligence methods drawing inspiration from human immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. CSA mainly generates new schemes by hyper-mutation operators which simulate the immune response process. However, these hyper-mutation operators, which usually perturb the antibodies in population, are semi-blind and not effective enough for complex optimization problems.. In this paper, we propose a hybrid learning clonal selection algorithm (HLCSA) by incorporating two learning mechanisms, Baldwinian learning and orthogonal learning, into CSA to guide the immune response process. Specifically, (1) Baldwinian learning is used to direct the genotypic changes based on the Baldwin effect, and this operator can enhance the antibody information by employing other antibodies' information to alter the search space;(2) Orthogonal learning operator is used to search the space defined by one antibody and its best Baldwinian learning vector. In HLCSA, the Baldwinian learning works for exploration (global search) while the orthogonal learning for exploitation (local refinement). Therefore, orthogonal learning can be viewed as the compensation for the search ability of Baldwinian learning. In order to validate the effectiveness of the proposed algorithm, a suite of sixteen benchmark test problems are fed into HLCSA. Experimental results show that HLCSA performs very well in solving most of the optimization problems. Therefore, HLCSA is an effective and robust algorithm for optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Artificial immune system clonal selection algorithm Baldwin effect Baldwinian learning Orthogonal learning Optimization

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An improved clonal selection algorithm using a tournament selection operator and its application to microstrip coupler design

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第3期25卷 1751-1761页

作者： Ulker, Ezgi Deniz European Univ Lefke Fac Engn Dept Comp Engn Lefke Northern Cyprus Turkey

The clonal selection algorithm (clonalG) is a nature-inspired metaheuristic algorithm that has been applied to various complex optimization problems from different fields of study. Tournament selection (TS) is a selection operator that is mainly used in genetic algorithms. In this paper, a novel improved clonal selection algorithm by using the TS operator (ICSAT) is introduced. To observe the improvement, ICSAT was first tested on selected benchmark functions and then to validate its efficiency ICSAT was applied to a microstrip coupler design problem. Although showing some disadvantages that generally exist in all modified algorithms, it is observed that ICSAT has a significant improvement on the performance of clonalG and can be a good candidate for real case optimization problems.

关键词： clonal selection algorithm improved metaheuristics microstrip coupler design tournament selection operator optimization

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Optimization of job shop scheduling problems using modified clonal selection algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第6期22卷 1528-1539页

作者： Atay, Yilmaz Kodaz, Halife Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best makespan values are reached in 7 out of 10 test problems (FT06, LA01, LA02, LA03, LA04, LA05, and ABZ6). Reasonable makespan values are reached for the remaining 3 problems (FT10, LA16, and ABZ5). The obtained results demonstrate that the developed system can be applied successfully to job shop scheduling problems.

关键词： Artificial immune system clonal selection algorithm job shop scheduling optimization

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An expanded lateral interactive clonal selection algorithm and its application

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2008年第8期E91A卷 2223-2231页

作者： Gao, Shangce Dai, Hongwei Zhang, Jianchen Tang, Zheng Toyama Univ Fac Engn Toyama 9308555 Japan

Based on the clonal selection principle proposed by Burnet, in the immune response process there is no crossover of genetic material between members of the repertoire, i.e., there is no knowledge communication during different elite pools in the previous clonal selection models. As a result, the search performance of these models is ineffective. To solve this problem, inspired by the concept of the idiotypic network theory, an expanded lateral interactive clonal selection algorithm (LICS) is put forward. In LICS, an antibody is matured not only through the somatic hypermutation and the receptor editing from the B cell, but also through the stimuli from other antibodies. The stimuli is realized by memorizing some common gene segment on the idiotypes, based on which a lateral interactive receptor editing operator is also introduced. Then, LICS is applied to several benchmark instances of the traveling salesman problem. Simulation results show the efficiency and robustness of LICS when compared to other traditional algorithms.

关键词： clonal selection algorithm somatic hypermutation receptor editing idiotype traveling salesman problem

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Bi-direction quantum crossover-based clonal selection algorithm and its applications

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第16期41卷 7248-7258页

作者： Dai, Hongwei Yang, Yu Li, Hui Li, Cunhua Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China Univ Manchester Sch Elect & Elect Engn Control Syst Ctr Manchester M13 9PL Lancs England

In order to improve the performance of quantum interference crossover, a bi-direction quantum crossover is proposed based on the quantum jump theory. The proposed crossover is inspired by the principle of quantum mechanics. That is, when an electron drops from a higher energy level to a lower energy level, energy is released by the atom. Also, energy is absorbed when it moves from a lower energy level to a higher energy level. The bi-direction quantum crossover is combined with clonal selection algorithm (CSA) to further enhance the performance of CSA. The effectiveness of the method is tested on a class of traveling salesman problems (TSP) and engineering practical problems of holes machining path planning (HMPP). Experimental results show that the proposed algorithm achieves a good balance between exploration and exploitation, and outweighs other CSAs and heuristic algorithms in terms of convergence speed and robustness. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： clonal selection algorithm Bi-direction quantum crossover Traveling salesman problem Holes machining path planning problem Multi-objective optimization

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