scholarly journals An artificial immune system algorithm for solving the uncapacitated single allocation p-Hub median problem

2021 ◽  
Vol 11 (3) ◽  
pp. 2293
Author(s):  
Fatima Zahraa Grine ◽  
Oulaid Kamach ◽  
Abdelhakim Khatab ◽  
Naoufal Sefiani
Keyword(s):  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Data Sets ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Hub Location ◽  
Median Problem ◽  
Solution Quality ◽  
Artificial Immune System Algorithm ◽  
Single Allocation

The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

scholarly journals Artificial Immune Systems for Solving the Uncapacitated Single-Allocation p-Hub Median Problem

2018 ◽  
Author(s):  
Gabriel L. Nobrega ◽  
Vinicius J. Tasso ◽  
Allan G. Souza ◽  
Stephanie A. Fernandez ◽  
Daniel G. Silva
Keyword(s):  
Network Design ◽  
Optimization Problems ◽  
Artificial Immune Systems ◽  
Research Interest ◽  
Artificial Immune ◽  
Hub Location ◽  
Median Problem ◽  
Immune Systems ◽  
Single Allocation

Optimization problems such as the Uncapacitated Single-Allocation p-Hub Median Problem represent good models for real network design issues, hence an increasing research interest has emerged. A good hub location reduces costs and improves the quality of delivered services on network-based systems. In this work, two artificial immune systems are employed in order to address the problem, where the numerical results indicate good quality of solutions.

Performance of Negative Selection Algorithms in Patient Detection and Classification

2018 ◽  
pp. 78-102
Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz
Keyword(s):  
Immune System ◽  
Error Detection ◽  
Classification Accuracy ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Detection Accuracy ◽  
Data Sets ◽  
Selection Algorithm ◽  
Artificial Immune

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

Cost optimization of water distribution networks by using artificial immune systems

2014 ◽  
Vol 64 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Miraç Eryiğit
Keyword(s):  
Water Distribution ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Proposed Model ◽  
The Impact

This study aims at the development of an optimization model based on artificial immune systems (AIS) to minimize cost designs of water distribution networks (WDNs). Clonal selection algorithm (Clonalg), a class of AIS, was used as an optimization technique in the model, and its mutation operation was modified to increase the diversity (search capability). EPANET, a widely known WDN simulator, was used in conjunction with the proposed model. The model was applied to four WDNs of Two-loop, Hanoi, Go Yang, New York City, and the results obtained were compared with other heuristic and mathematical optimization models in the related literature, such as harmony search, genetic algorithm, immune algorithm, shuffled complex evolution, differential evolution, and non-linear programming-Lagrangian algorithm. Furthermore, the modified Clonalg was compared with the classic Clonalg in order to demonstrate the impact of the modification on the diversity. The proposed model appeared to be promising in terms of cost designs of WDNs.

scholarly journals Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6351
Author(s):  
Łukasz Rokicki
Keyword(s):  
Immune System ◽  
Power Systems ◽  
Evolutionary Algorithm ◽  
Distribution System ◽  
Artificial Immune System ◽  
Low Voltage ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Clonal Selection Algorithm ◽  
Artificial Immune

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

2016 ◽  
Vol 30 (09) ◽  
pp. 1660008 ◽  
Author(s):  
Ayodele Lasisi ◽  
Rozaida Ghazali ◽  
Mustafa Mat Deris ◽  
Tutut Herawan ◽  
Fola Lasisi
Keyword(s):  
Feature Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Recognition System ◽  
Clonal Selection Algorithm ◽  
Immune Recognition ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Detection Rates ◽  
Clonal Selection Theory

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with CLONALG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-CLONALG, FRFS-AIRS, VQRS-CLONALG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

scholarly journals Multithreshold Segmentation Based on Artificial Immune Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Cuevas ◽  
Valentin Osuna-Enciso ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Humberto Sossa
Keyword(s):  
Initial Conditions ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Gaussian Function ◽  
Gaussian Mixture ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems ◽  
Gradient Based ◽  
Low Sensitivity

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.

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