Review on the Bat Algorithm and Various Metaheuristic Techniques for Efficient Parallel Scheduling

The various meta-heuristic techniques for cloud and grid environment are: Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search, Firefly Algorithm, BAT Algorithm and many more. So this paper represents the two types of meta-heuristic techniques, i.e. BAT algorithm and Genetic Algorithm. The different types of methods which comprise meta-heuristic algorithms range from simple local search approach to complex learning methods. It also shows the comparison of various techniques, i.e. one of the BAT intelligence (BI) and Genetic algorithm (GA) are used to figure out single objective multiprocessor scheduling problem utilizing objective functions as makespan, tardiness and power consumption. BI depicts significant improvement in terms of solution quality when compared with GA in terms of contradictory between makespan and energy furthermore among tardiness and energy. Keywords— Bat Algorithm, Constant Absolute Target Direction (CATD) Technique, Metaheuristics, Dynamic Voltage Scaling, Energy-Aware Multiprocessor Scheduling, Energy Utilization, Normalized Weight Additive Utility Function (NWAUF)