A General Framework for Multi-Objective Optimization Immune Algorithms

Abstract

Abstract-Artificial Immune System (AIS) is a hotspot in the area of Co mputational Intelligence. While the Multi-Objective Optimizat ion (MOP) problem is one of the most widely applied NP-Co mp lete problems. During the past decade more than ten kinds of Mult i-Objective optimization algorith ms 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 A lgorith ms, which summarizes a uniform outline of this kind of algorithms and gives a description of its principles, main ly used operators and processing methods. Then we imp lement the proposed framework and build four typical immune algorithms on it: CLONA LG, 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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“…Afshari and Sajedi [2] introduced two new mutation methods, namely Shift Change method and Inverse method in Jobshop scheduling and a vaccination method, to achieve more than one optimal solution concurrently and release from local optimum. Yunfang et al [3] proposed a general framework of Multi-Objective Immune Algorithms, which reviews a uniform outline of this kind of algorithms and gives a description of its principles, mainly used operators and processing methods. Gopinath et al [4] developed artificial immune algorithm based hybrid algorithm to optimize the scheduling problem.…”