Efficient Metaheuristic Population-Based and Deterministic Algorithm for Resource Provisioning Using Ant Colony Optimization and Spanning Tree

2. Background

ACO is a member of the metaheuristic algorithm that follows real ant colony foraging behavior principle. Ant’s behavioral means of communication is by through pheromone (chemical substance) that enables them to find shortest path from their nest to food source. Secreted pheromone at the cause of searching their food is used for exchanging information while trailing for the shortest path. Path with the highest concentration of the substance is regarded as the shortest path. ACO has flourished in areas for solving job shop scheduling problem (Hadi and Reza, 2011), multidimensional knapsack problem (Ines et al., 2004), Traveling Salesman Problem (TSP) (Harshitha and Beena, 2017), scheduling of tasks in grid and Cloud (Tawfeeq et al., 2013), quadratic assignment problem (Rainer, 2013) and many more. The algorithm performs exigently in solving problems having to do with discrete optimization that needs to discover short routes to goals. Despite the plethora adoptions of ACO in such fields, the algorithm still needs some improvements in handling transition loops that leads to longer convergence time, initial population generation and pheromone evaporation. Best performing techniques make intensive use of its optional local search phase and proper initial solutions generation (Dorigo, 2006).