Optimal design using clonal selection algorithm

Authors:
Yi-Hui Su;Wen-Jye Shyr;Te-Jen Su
Affiliations:
Dept. of Information Management, Tzu-Hui Institute of Technology, Pintung, Taiwan, R.O.C.;Dept. of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan, R.O.C.;Dept. of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, R.O.C.
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

Citing 4
Cited 2

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Artficial Immune Systems and Their Applications

Artficial Immune Systems and Their Applications
Genetic Algorithms and the Immune System

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation

A Novel Clonal Selection Algorithm and Its Application to Traveling Salesman Problem

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Multiple swarms immune clonal quantum-behaved particle swarm optimization algorithm and the wavelet in the application of forecasting foundation settlement

CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. This paper synthesizes the advantages of clonal selection algorithm and proposed optimal design problem using clonal selection algorithm which is a basis of the immune system. CSA, the essence of immune algorithm, is effective to solve optimal problem. The clonal selection algorithm is highly parallel and presents a fine tractability in terms of computational cost. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution. Clonal selection algorithm and genetic algorithm are used to reach the optimization performances for two numerical function. Then those results are compared each other. These proposed algorithms are shown to be an evolutionary strategy capable of solving optimal design problem.