A novel micro-population immune multiobjective optimization algorithm 

 Abstract

 In this paper, we present a novel immune multiobjective optimization algorithm 
 based on micro-population, which adopts a novel adaptive mutation operator for 
 local search and an efficient fine-grained selection operator for archive update. 
 With the external archive for storing nondominated individuals, the population 
 diversity can be well preserved using an efficient fine-grained selection procedure 
 performed on the micro-population. The adaptive mutation operator is executed 
 according to the fitness values, which promotes to use relatively large steps for 
 boundary and less-crowded individuals in high probability. Therefore, the exploratory 
 capabilities are enhanced. When comparing the proposed algorithm with a recently 
 proposed immune multiobjective algorithm and a scatter search multiobjective algorithm 
 in various benchmark functions, simulations show that the proposed algorithm not only 
 improves convergence ability but also preserves population diversity adequately in most 
 cases.