用于求解多目标优化问题的克隆选择算法

提出一种用于求解多目标优化问题的新算法，将抗体群中的抗体分为支配抗体和非支配抗体代替传统算法中对所有个体分配适应度值，以适应多目标优化问题存在一系列无法相互比较的Pareto-最优解的特点;对非支配抗体进行选择,有利于算法向着理想Pareto-前端搜索，而且加快了收敛速度;克隆操作实现了全局择优，有利于得到分布较广的Pareto-前端;采用非一致性变异操作以提高算法的局部搜索能力，有利于所得解的多样性．与已有算法相比，新算法所得的最优解分布最广，很大程度上支配着其他算法得到的最优解，评价指标S降低到了3%以下．

Conal selection algorithm for multi-objective optimization problems

A new algorithm for multi-objective optimization problems is proposed.The antibodies in the antibody population are divided into dominated ones and non-dominated ones,which is suitable for the characteristic that one multi-objective optimization problem has a series Pareto-optimal solutions.Selecting of the non-dominated antibodies guarantees the convergence to the true Pareto-front and the convergence speed.The clonal operation implements the searching for optimal solutions in the global region and is available for getting a widely spread Pareto-front.Adopting the non-uniform mutation operation improves the searching for optimal solutions in the local region and assures the diversity of the solutions.Compared with the existing algorithms,simulation results show that the solutions obtained by the new algorithm are most widely spread,dominate those gained by the other algorithms to some extent and depress the metric S to less than 3%.