Application of Ant Colony Algorithm in Multi-objective Optimization Problems
Abstract: In actual
application and scientific research, multi-objective optimization is an extremely
important research subject. In reality, many issues are related to the
simultaneous optimization under multi-objective conditions. The research
subject of multi-objective optimization is getting increasing attention. In
order to better solve some nonlinear, restricted complex multi-objective
optimization problems, based on the current studies of multi-objective
optimization and evolutionary algorithm, this paper applies the ant colony
algorithm to multi-objective optimization, and proves through experiments that
multi-objective ant colony algorithm can converge the real Pareto front of the
standard test function more quickly and accurately, and can also maintain the
distributivity of the better solution.
Author: Juan Li, Xianghong
Tian
Journal Code: jptkomputergg150127
