An Adaptive Multi-objective Immune Optimization Algorithm

It is difficult for traditional search methods to solve multi-objective optimization problems. Based on the idea of clonal selection principle, we present an adaptive multi-objective immune optimization algorithm (AMIOA) for function optimization problems and analyze its powerful performance from th...

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Bibliographic Details
Published in:CASE 2009 : 2009 IITA International Conference on Control, Automation and Systems Engineering : proceedings, 11-12 July 2009, Zhangjiajie, China pp. 140 - 143
Main Author: Lu Hong
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2009
Subjects:
Adaptive control
Adaptive systems
artificial immune systems
Automatic control
Automation
clonal selection principle
Control systems
Immune system
multi-objective function optimization
Optimization methods
Power engineering and energy
Programmable control
Systems engineering and theory
ISBN:0769537286, 9780769537283
Online Access:Get full text
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Summary:It is difficult for traditional search methods to solve multi-objective optimization problems. Based on the idea of clonal selection principle, we present an adaptive multi-objective immune optimization algorithm (AMIOA) for function optimization problems and analyze its powerful performance from the immune system point of view. The main feature of the algorithm is the global search performance and the solution sets produced are highly competitive in terms of convergence, diversity and distribution. The comparative simulation results show that the proposed algorithm not only can obtain a set of solutions including the global optimum and multiple local optima, but also has much less computational cost than other algorithms.
ISBN:0769537286
9780769537283
DOI:10.1109/CASE.2009.133