A General Framework for Multi-Objective Optimization Immune Algorithms

A General Framework for Multi-Objective Optimization Immune Algorithms

Автор: Chen Yunfang

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 6 vol.4, 2012 года.

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Artificial Immune System (AIS) is a hotspot in the area of Computational Intelligence. While the Multi-Objective Optimization (MOP) problem is one of the most widely applied NP-Complete problems. During the past decade more than ten kinds of Multi-Objective optimization algorithms based on AIS were proposed and showed outstanding abilities in solving this kind of problem. The paper presents a general framework of Multi-Objective Immune Algorithms, which summarizes a uniform outline of this kind of algorithms and gives a description of its principles, mainly used operators and processing methods. Then we implement the proposed framework and build four typical immune algorithms on it: CLONALG, MISA, NNIA and CMOIA. The experiment results showed the framework is very suitable to develop the various multi-objective optimization immune algorithms.

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Multi-Objective Optimization, Artificial Immune Systems, Algorithms Framework

Короткий адрес: https://sciup.org/15010266

IDR: 15010266

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