Abstract
Ant Colony Optimization (ACO) is a stochastic search method that mimic the social behavior of real ants colonies, which manage to establish the shortest rout to feeding sources and back. Such algorithms have been developed to arrive at near-optimal solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. On this paper is proposed an ant algorithm with semi-random start. Several start strategies are prepared at the basis of the start nodes estimation. There are several parameters which manage the starting strategies. In this work we focus on influence on the quality of the achieved solutions of the parameters which shows the percentage of the solutions classified as good and as bad respectively. This new technique is tested on Multiple Knapsack Problem (MKP).