scholarly journals Artificial Immune System Applied to Job Shop Scheduling

2021 ◽  
pp. 15-22
Author(s):  
Steven Kosasih ◽  
◽  
Cecilia E. Nugraheni ◽  
Luciana Abednego
Keyword(s):  
Immune System ◽  
Lower Bound ◽  
Upper Bound ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune

Job Shop Scheduling is a problem to schedule n number of jobs in m number of machines with a different order of processing. Each machine processes exactly one job at a time. Each job will be processed in every machine once. When a machine is processing one particular job then the other machine can’t process the same job. Different schedule’s order might produce different total processing time. The result of this scheduling problem will be total processing time and schedule’s order. This paper uses clonal selection as the algorithm to solve this problem. The clonal selection algorithm comes from the concept of an artificial immune system. It's developed by copying a human’s immune system behavior. A human’s immune system can differentiate foreign objects and eliminate the objects by creating an antibody. An antibody will go to a cloning process and will mutate to further enhance itself. Clonal selection algorithm applies this cloning and mutation principle to find the most optimal solution. The goal is to find the best schedule’s order and makespan. Taillard’s benchmark is used to verify the quality of the result. To compare the result, we use two values: the upper bound and the lower bound. The upper bound is used to describe the best result of a scheduling problem that has been conducted using a certain environment. On the contrary, the lower bound shows the worst. Experiments on changing the algorithm's parameters are also conducted to measure the quality of the program. The parameters are the number of iterations, mutations, and clone numbers. According to the experiment's results, the higher the number of iteration, mutation rate, and clone number, the better solution for the problem. Clonal selection algorithm has not been able to keep up with upper bound or lower bound values from Taillard’s case. Therefore, parameters need to be increased significantly to increase the chance to produce the optimum result. The higher number of parameters used means the longer time needed to produce the result.

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.

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 APPLICATION OF CLONAL SELECTION ALGORITHM FOR PID CONTROLLER SYNTHESIS OF MIMO SYSTEMS IN OIL AND GAS INDUSTRY

2019 ◽  
Vol 9 (3) ◽  
pp. 50-53
Author(s):  
Olga Shiryayeva ◽  
Timur Samigulin
Keyword(s):  
Oil And Gas ◽  
Mimo Systems ◽  
Clonal Selection ◽  
Oil And Gas Industry ◽  
Controller Synthesis ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Gas Industry ◽  
Smart Technologies

This paper presents the results of the Smart technologies application to the synthesis of MIMO-systems in oil and gas industry. In particular, there is considered a multidimensional multiply connected system for gas distillation process control through a distillation column with regulators configured on the basis of Smart-technologies – clonal selection algorithm (CLONALG) of an artificial immune system (AIS).

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