Ant colony optimization with partial-complete searching for attribute reduction

The time-cost-sensitive attribute reduction problem is more challenging than the classical reduct problem since the optimal solution is sparser. Ant colony optimization (ACO) is an effective approach to this problem. However, the efficiency is unsatisfactory since each ant needs to search for a complete solution. In this paper, we propose a partial-complete searching technique for ACO and design the APC algorithm. Partial searching is undertaken by pioneer ants through selecting only a few attributes to save time, while complete searching is undertaken by harvester ants for complete solutions. Experiments are undertaken on seven real-world and a set of artificial datasets with various settings of costs. Compared with two bio-inspired and two greedy algorithms, APC is more efficient while obtaining the same level of quality metrics. The APC algorithm can be also extended for other combinatorial optimization problems.