Based on clonal selection principle, this paper proposes an immune-inspired evolution strategy (IIES) for constrained optimization problems with two improvements. Firstly, in order to enhance global search capability, more clones are produced by individuals that have far-off nearest neighbors in the less-crowed regions. On the other hand, immune update mechanism is proposed to replace the worst individuals in clone population with the best individuals stored in immune memory in every generation. Therefore, search direction can always focus on the fittest individuals. These proposals are able to avoid being trapped in local optimal regions and remarkably enhance global search capability. In order to examine the optimization performance of IIES, 13 well-known benchmark test functions are used. When comparing with various state-of-the-arts and recently proposed competent algorithms, simulation results show that IIES performs better or comparably in most cases.
