A Membrane-Genetics Algorithm for Multi-Objective Optimization Problems

This paper proposes a multi-objective optimization algorithm based on the membrane computing. Inspired by the theory of membrane optimization, the membrane structure, multiple sets and reaction rules is employed to tackle multi-objective optimization issues. Aiming at adaptability of algorithm, the cross-over and mutation mechanism of the genetic algorithm are introduced to combine with membrane framework. Moreover, for the sake of improving the diversity of global search solution, the non-dominated sorting and crowding distance are used to update external archive. The experimental results demonstrate that the proposed algorithm is not only practicable and efficient but also capable of obtaining the approximate Pareto front in KUR and ZDT test function.