A Quantum Chaos Clonal Multiobjective Evolutionary Method Reasearch

2016 ◽  
Vol 3 (1) ◽  
pp. 226
Author(s):  
Shuyue Wu
Keyword(s):  
Quantum Computing ◽  
Immune System ◽  
Quantum Chaos ◽  
Artificial Immune System ◽  
Population Distribution ◽  
Clonal Selection ◽  
Initial Population ◽  
Artificial Immune ◽  
Clonal Selection Theory ◽  
Chaos Search

A quantum chaos cloning multi-objective evolutionary algorithm was proposed herein based on chaos search ergodicity, quantum computing efficiency and clonal selection theory of antibodies in artificial immune system. The qubits encoded initial population is used in the new algorithm, Chaos quantum rotation gates are introduced to update individuals, crowding distance  is used  to keep solution  population distribution and diversity. Theoretical analysis and simulation show the effectiveness of the algorithm.

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.

Artificial Immune System for Multi-Area Economic Dispatch

2013 ◽  
Vol 14 (6) ◽  
pp. 581-590 ◽  
Author(s):  
Shankha Suvra De ◽  
Abhik Hazra ◽  
Mousumi Basu
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Economic Dispatch ◽  
Artificial Immune ◽  
Promising Alternative ◽  
Alternative Approach ◽  
Real Coded Genetic Algorithm ◽  
Tournament Selection

Abstract This article presents artificial immune system for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial immune system is based on the clonal selection principle which implements adaptive cloning, hyper mutation, aging operator and tournament selection. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real-coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system.

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