An Improved Clonal Selection Algorithm and Its Application in Function Optimization Problems

Combining the advantage of the interior study mechanism of biological immune system and evolutionary algorithm, this paper proposed an improved clonal selection algorithm (ICSA) which can solve the problem of easily being trapped in local minima and slow convergence of clonal selection algorithm. The improved algorithm included orthogonal crossover, simplex crossover, clone and selection. The idea of evolutionary computation was integrated into clone selection and a new mutation operator was proposed. The new algorithm can guarantee the diversity of the population and improve the global search ability. Theoretical analysis has proved that ICSA converges to the global optimum. Different functions were utilized to test this method and the simulation results have shown that the proposed ICSA algorithm has good performance.