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.
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