A Faster Clonal Selection Algorithm for Expensive Optimization Problems

摘要

Artificial Immune Systems (AISs) are computational methods, inspired by the biological immune system, that can be applied to solve optimization problems. In this paper we propose the use of a similarity-based surrogate model in conjunction with a clonal selection algorithm in order to improve its performance when solving optimization problems involving computationally expensive objective functions. Computational experiments to assess the performance of the proposed procedure using 23 test-problems from the literature are presented.