Abstract A clonal selection algorithm is proposed, called clonal selection algorithm based on grade variation (CSABGV). To improve the effectiveness of the variation, the variability scale is divided into several levels: low grade variant is conducive to jump out of local optimal solution and to achieve global optimization while high grade variation is in favor of local optimization. In addition, the algorithm remembers and uses the information of variation grade of the parent antibody, and develops effective mutation strategy to guide mutation of antibodies. The performance of the proposed algorithm is compared with five benchmark functions and other optimization algorithms. Experimental results show that CSABGV has the characteristics of rapid convergence, powerful global search capability, high precision and good robustness.
2011, Vol. 24 
