基于合作模型的协同免疫多目标优化算法

本文针对多目标优化问题Pareto最优解集合(PS)的分布特点，构造了一种基于新的子任务划分方法的合作型协同进化模型，并将该模型引入人工免疫系统中，提出了一种基于合作模型的协同免疫多目标优化算法(A Cooperative Immune Coevolutionary Algorithm for Multiobjective Optimization,CICAMO).CICAMO算法运用Tchebycheff分解方法进行子种群划分，然后对各个子种群建立线性概率统计模型分段逼近整个PS，在抗体繁殖上结合了克隆选择和模型采样两种方式.实验结果表明，CICAMO算法在求解质量和收敛速度上均表现良好，尤其对于决策变量非线性相关的多目标优化问题，性能尤为突出.

A Cooperative Immune Coevolutionary Algorithm for Multi-Objective Optimization

According to the distribution characteristics of the Pareto set (PS) of multi-objective optimization problems (MOPs),a cooperative coevolutionary model with new problem decomposition method was designed.By introducing the proposed coevolutionary model into artificial immune system,a cooperative immune coevolutionary algorithm for multi-objective optimization (CICAMO) was proposed.In CICAMO,the Tchebycheff decomposition method is employed to divide sub-populations at first,and then linear probabilistic models are built for each sub-population to piecewise approximate the distribution of the whole PS.In antibody reproducing step,two types of approaches based on clonal selection and model sampling are employed.Experimental results indicate that CICAMO can achieve a good performance in terms of both solution quality and convergence rate,especially when solving MOPs with non-linear relationship between decision variables.