Differential clonal selection algorithm for solving constrained optimization problems

In this paper, a differential clonal selection algorithm (DCSA) combined with an adaptive penalty function method is proposed for solving constrained optimization problems. In order to improve the diversity of the solution, a minority part of the antibodies located in sparse region are selected to do proportional cloning according to their minimum neighbor distance values. Comparison is made to four state-of-the-art algorithms in solving eleven well-known standard test problems. Simulation results show that DCSA performs better or similarly than the four approaches.