期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2015
卷号：13
期号：3
页码：1029-1036
DOI：10.12928/telkomnika.v13i3.1806
出版社：Universitas Ahmad Dahlan
摘要：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.
其他摘要：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.
