Immune System Multiobjective Optimization Algorithm for DTLZ Problems

Bin Zhang, Weihua Ren, Lihua Zhao, Xiaozheng Deng
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引用次数: 4

Abstract

Based on Artificial Immune System, a novel Immune System Multiobjective Optimization Algorithm (ISMOA) is proposed. ISMOA presents nondominated sorting clonal selection operation, preserving the diversity of the population and more uniformly distributing global Pareto optimal solutions. The comparisons of ISMOA with NSGAII in DTLZ problems suggest that ISMOA clearly outperforms in converging towards the true front coverage and finding the spread of solutions.
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DTLZ问题的免疫系统多目标优化算法
提出了一种基于人工免疫系统的免疫系统多目标优化算法(ISMOA)。ISMOA采用非显性分选克隆选择操作,既保持种群的多样性,又使全局Pareto最优解分布更加均匀。ISMOA与NSGAII在DTLZ问题中的比较表明,ISMOA在收敛于真实前沿覆盖和寻找解决方案的传播方面明显优于ISMOA。
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