Abstract: Aiming at multi-objective optimization problem, this paper proposes an Ant Colony Algorithm(ACA) for solving Multi-objective Optimization Problem(MOPACA). An improved pheromone updating process based on continuous space is described. Two moving strategies are used in the searching process to ensure better solutions. Convergence property of the algorithm is analyzed. Preliminary simulation results of two benchmark functions show the feasibility of the algorithm.

Key words: Ant Colony Algorithm(ACA), multi-objective optimization, convergence analysis

摘要： 针对多目标优化问题，提出一种用于求解多目标优化问题的蚁群算法。该算法定义连续空间内求解多目标优化问题的蚁群算法的信息素更新方式，根据信息素的概率转移和随机选择转移策略指导蚂蚁进行搜索，保证获得的Pareto前沿的均匀性以及Pareto解集的多样性。对算法的收敛性进行分析，利用2个测试函数验证算法的有效性。

关键词: 蚁群算法, 多目标优化, 收敛性分析

CLC Number: 

• TP18