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A clonal selection algorithm for Energy-Efficient Mobile Agent Itinerary Planning in Wireless Sensor Networks

MOBILE NETWORKS & APPLICATIONS 2018年第5期23卷 1233-1246页

作者： Chou, Yu-Cheng Nakajima, Madoka Natl Sun Yat Sen Univ Inst Undersea Technol 70 Lienhai Rd Kaohsiung 80424 Taiwan

This paper presents a new immune inspired algorithm, the clonal selection algorithm for Multi-agent Itinerary Planning (CSA-MIP), to solve the MIP problem in wireless sensor networks. CSA-MIP has two computational stages called Stage I and Stage II. When only Stage I is activated in CSA-MIP, in the obtained solutions, the difference between the maximum and minimum numbers of sensor nodes, visited by each mobile agent, is relatively large. When only Stage II is activated in CSA-MIP, in the obtained solutions, the number of mobile agents is relatively small. When both stages are activated in CSA-MIP, in the obtained solutions, the diversity in the number of mobile agents can be achieved and the unbalance in the number of sensor nodes visited by each mobile agent can be reduced, which indicate a higher possibility to obtain solutions of better quality. Moreover, according to the simulation results and analysis of computational complexity, CSA-MIP is shown to perform better than GA-MIP in terms of solution quality and computational efficiency.

关键词： Wireless sensor network Mobile agent Multi-agent itinerary planning Genetic algorithm Artificial immune system clonal selection algorithm

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Solving DNA Motif Discovery Problem using Improved clonal selection algorithm with Tournament selection Operator DNA Motif Discovery with clonal selection algorithm

Solving DNA Motif Discovery Problem using Improved Clonal Se...

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作者： Ezgi Deniz ülker Department of Computer Engineering European University of Lefke

Identifying the regulatory elements in deoxyribonucleic acid(DNA) is a challenging area of research. The order of nucleotides in a DNA sequence determines the genetic functionality. The common nucleotides called motifs which have short sizes are placed in large number of sequences. Because of the difficulty to obtain the motifs among the large number of sequences, Motif Discovery Problem(MDP) is referred as a NP-hard problem. Since this problem is considered an open area for researchers, the evolutionary algorithms can be used to bring new solutions to this field. In this paper, Improved clonal selection algorithm with Tournament selection operator(ICSAT) is adapted to solve MDP by using real DNA sequences and compared with some other algorithms which are particularly designed to solve it. The results denote that ICSAT obtains motifs between the large sequences and it produces efficient results in terms of motif length and similarity values with respect to the compared algorithms. It can be used as a good candidate for solving these kinds of problems in bioinformatics.

关键词： Biological optimization clonal selection algorithm Motif discovery Tournament selection

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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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Solving Multi Objective ORPD Problem Using AIS Based clonal selection algorithm with UPFC

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JOURNAL OF ELECTRICAL SYSTEMS 2017年第1期13卷 27-42页

作者： Rao, B. Srinivasa Lingaiah, A. Bala Naga VR Siddhartha Engn Coll Vijayawada 520007 AP India

In this paper, a solution for the multi objective optimal reactive power dispatch problem by using an artificial immune system (AIS) based clonal selection algorithm was presented. The proposed AIS based clonal selection algorithm uses cloning of antibodies and followed by hyper maturation to minimize the voltage stability index (L-index, voltage deviations at all load buses and the transmission real power losses by incorporating the multi type FACTS device namely the UPFC. The proposed algorithm also uses concepts of non dominated sorting and crowding distance comparison procedures to solve the multi objective optimization problem. Finally, a fuzzy decision maker strategy is applied to find the best compromise solution. The algorithm was implemented and tested on two standard IEEE 30-bus and 57-bus test systems with UPFC. The proposed results are compared with and without placing the UPFC by considering two objectives for optimization.

关键词： Optimal Reactive Power Dispatch clonal selection algorithm UPFC Multi-objective

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Average convergence rate estimation of clonal selection algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2017年第9期9卷 1-6页

作者： Hong, Lu Wang, Jingzhuo Zhang, Ming Dai, Hongwei Cheng, Jiali Huaihai Inst Technol Dept Elect Engn Lianyungang 222005 Jiangsu Peoples R China Huaihai Inst Technol Marine Resources Dev Inst Jiangsu Lianyungang Peoples R China

Considering that average convergence rate estimation of clonal selection algorithms is a difficult problem and is still in its infancy, this article researches the convergence rate of an elitist clonal selection algorithm. It derives the best individual transition probability matrix from the directional transition probability of best individuals in algorithm populations and constructs matrix norms that meet certain requirements to resolve difficulties in calculating the matrix caused by large algorithm populations in practical applications, thereby proposing a simple and effective method of estimating average convergence rate of the algorithm. In addition, simulation experiments are performed to validate universality and validity of the estimation method.

关键词： clonal selection algorithm elitist strategy average rate of convergence transition probability matrix norm

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Improved clonal selection algorithm optimizing Neural Network for solving terminal anti-missile collaborative intercepting assistant decision-making model

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第3期8卷 210-226页

作者： Jinke Xiao Weimin Li Xinrong Xiao Air And Missile Defense College Air Force Engineering University Xi’an 710051 P.R.China School of Light Industry And Food Sciences South China University Of Technology Guangzhou 510640 P.R.China

Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is onekey problem to enhance operational *** decision-making model has been constructed after analysis on collaborative intercepting principle;then Improved clonal selection algorithm Optimizing Neural Network(IclonalGNN)is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity,introducing modified combination operator to make use of the information before crossover and mutation,introducing population update operator into traditional clonalG to optimize Neural Network *** simulation confirms the superiority and practicability of the assistant decision-making model solved by IclonalG-NN.

关键词： Terminal anti-missile system collaborative intercepting assistant decisionmaking clonal selection algorithm neural network

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A clonal selection algorithm for the generalized cell formation problem considering machine reliability and alternative routings

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2017年第4-5期11卷 545-556页

作者： Karoum, Bouchra Bouazza Elbenani, Youssef Mohammed V Univ Rabat Res Comp Sci Lab LRI Fac Sci BP 1014 Rabat Morocco

The cell formation is the first step in the design of Cellular Manufacturing systems. It consists of grouping parts with similar processing needs into cells and identifying the set of machines needed to process these parts. The aim is to minimize the material handling costs and maximize the use of the machines. In this paper, the machine reliability and the alternative process routings are taken into account to form the production cells. The presence of these factors in addition to the production volume, operation sequence and production time makes the problem more realistic but also more complex. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a modified version of the clonal selection algorithm is introduced and a local search mechanism is adopted in this paper. The obtained results are compared with those of the Branch and Bound (B&B) method using LINGO software. The comparison reveals the effectiveness and the efficiency of the proposed method in terms of both solution quality and computation time required.

关键词： Cell formation problem clonal selection algorithm Material handling costs Alternative routings Machine reliability Production volume Production time

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clonal selection algorithm with Adaptive Levy Mutation Operator 16

Clonal Selection Algorithm with Adaptive Levy Mutation Opera...

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1st International Conference on Informatics and Analytics (ICIA)

作者： Megala, T. Kashyap, Moganti Vivekanandan, K. Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry 605014 India

ISBN: (纸本)9781450347563

This paper studies clonal selection algorithm with different hypermutation operator such as Gaussian, Cauchy and Levy Mutation operator and employs an adaptive Levy hypermutation operator for solving complex numerical optimization problem. Levy mutation operator is based on Levy probability distribution which is capable of generating individuals far away from its parents. The proposed adaptive Levy hypermutation operator is applied to a set of benchmark functions. From the empirical evidence it is inferred that the proposed hypermutation operator performs better even for functions with many local optima than the Gaussian and Cauchy mutation operator.

关键词： Gaussian Mutation Cauchy Mutation Levy Mutation clonal selection algorithm

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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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Reconfiguration of Radial Distribution Systems with Variable Demands Using the clonal selection algorithm and the Specialized Genetic algorithm of Chu-Beasley

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2016年第6期27卷 689-701页

作者： Souza, Simone S. F. Romero, Ruben Pereira, Jorge Saraiva, Joao T. Univ Estadual Paulista Dept Elect Engn UNESP BR-15385000 Ilha Solteira SP Brazil INESC TEC Ave Dr Roberto Frias 378 P-4200465 Oporto Portugal Univ Porto Ave Dr Roberto Frias 378 P-4200465 Oporto Portugal

This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the clonalG and the SGACB algorithms. The clonalG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproducing the main properties and functions of the system. The SGACB is an optimization algorithm inspired by natural selection and the evolution of species. The reconfiguration problem with variable demands is a complex combinatorial problem that aims at identifying the best radial topology for an EDS, while satisfying all technical constraints at every demand level and minimizing the cost of energy losses in a given operation period. Both algorithms were implemented in C++ and test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes, in order to validate the proposed methods. The obtained results were compared with results available in the literature in order to verify the efficiency of the proposed approaches.

关键词： Distribution systems reconfiguration Variable demands clonal selection algorithm Specialized genetic algorithm of Chu-Beasley Artificial immune systems Metaheuristics

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