On the Dynamic Ant Colony Algorithm Optimization Based on Multi-pheromones

In this paper, an algorithm DACO (dynamic ant colony optimization algorithm based on multi- pheromones) is put forward to apply to the dynamics of web services state and QoS in service composition optimization. In order to denote users' needs more accurately, this algorithm sets multiple pheromones. The DACO is also improved based on experiment in order to make it better and faster converge to optimization value. Simulation experiment in this paper shows that the DACO is more effective than Ant Colony Algorithm and a Genetic Algorithm applied to services composition.

1. Introduction

Optimization has been a popular research topic for decades. Particle Swarm Optimization (PSO) is now established as an efficient optimization algorithm for static functions in a variety of contexts [1]. With the dynamic optimization problems appearing, the PSO is also applied in dynamic optimization [2]. The dynamic optimization problems have been changing over time, which causes changes in the position of optima as well as the characteristics of the search space. This leads to the fact that existing optima may disappear, while new optima may appear. Optimization under the dynamic environments is a challenging task that attracts great attention.