Generalized Ant Colony Optimizer: swarm-based meta-heuristic algorithm for cloud services execution

摘要

This work presents a swarm-based meta-heuristic technique known as Generalized Ant Colony Optimizer (GACO). It is a hybrid approach which consists of Simple Ant Colony Optimization and Global Colony Optimization concepts. The main concept behind GACO is the foraging behavior of ants. GACO operates in the following four phases: Creation of a new colony, search of nearest food location, balance the solution, and updating of pheromone. GACO has been tested on seventeen well recognized standard benchmark functions and its results have been compared with three different meta-heuristic algorithms namely as Genetic Algorithm, Particle Swarm Optimization and Artificial Bee Colony. The performance metrics such as average and standard deviation are computed and evaluated with respect to these metrics. The proposed GACO performs better in comparison to the aforementioned algorithms. The proposed algorithm optimizes the cloud resource allocation problem and gives better results with unknown search spaces.