快速收敛的克隆选择算法

Fast convergence clonal selection algorithm

针对克隆选择算法抗体群多样性有限和容易早熟等问题,提出了快速收敛的克隆选择算法。引入新型克隆算子,维持了抗体间促进与抑制的平衡;为了跳出局部最优,结合云模型的特征,给出了云自适应变异算子,与抗体重组算子合作,有效地增加了抗体的多样性,进而增强了算法的全局和局部搜索能力。对标准测试函数进行了仿真实验,并与其它算法进行了比较,比较结果表明,该算法寻优精度高、鲁棒性好、收敛速度快、时间复杂度不高。

Aiming at the shortcoming of clonal selection algorithm （CLONALG）, such as limited diversity of antibody population and prematurity, fast convergence clonal selection algorithm is proposed. First, new clonal operator is introduced to keep the balance of facilitation and inhibition between antibody; then to go out of the local optimum in CLONALG, combined with charac- teristics of cloud model, cloud self-adapting mutation operator is presented and the diversity of antibody effectively is increased by cooperating with antiboby recombing operator and the gobal and local search ability is enhanced. Through testing the perfor- mance of the proposed approach on benchmark functions and comparing with other meta-heuristics, the result of simulation shows that proposed approach is swarm intelligent optimization algorithm with high precise iteration, good robustness, fast con- vergence and not high time complexity.