A population adaptive based immune algorithm for solving multi-objective optimization problems

The primary objective of this paper is to put forward a general frameworkunder which clear definitions of immune operators and their roles areprovided. To this aim, a novel Population Adaptive Based Immune Algorithm(PAIA) inspired by Clonal Selection and Immune Network theories for solvingmulti-objective optimization problems (MOP) is proposed. The algorithm isshown to be insensitive to the initial population size; the population and clonesize are adaptive with respect to the search process and the problem at hand. Itis argued that the algorithm can largely reduce the number of evaluation timesand is more consistent with the vertebrate immune system than the previouslyproposed algorithms. Preliminary results suggest that the algorithm is a valuablealternative to already established evolutionary based optimization algorithms,such as NSGA II, SPEA and VIS.