Research of a Self-adaptive Mixed-Variable Multi-objective Ant Colony Optimization Algorithm

Abstract

Multi-objective optimization (MOO) problems are often encountered in engineering calculations. Commonly used MOO algorithms include classical algorithms and modern heuristic algorithms. Modern heuristic algorithms can find the Pareto solution set, and are used most widely at present. This paper improves the ACO MV algorithm which is only suitable for solving mixed-variable single-objective optimization (SOO) problems, and proposes a MOACO MV algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACO MV algorithm performance on parameter setting, a SAMOACO MV algorithm using self-adaptive parameter setting scheme is proposed. Furthermore, the paper also designs one mixed-variable MOO benchmark for purpose to test and compare the performance of SAMOACO MV algorithm. The experiments indicate that SAMOACO MV algorithm has excellent comprehensive performance and is an ideal choice for solving mixed variable MOO problems.