An Agent-Based Approach for Dynamic Load Balancing Using Hybrid NSGA II

摘要: To date, there have been many observations about load balancing on different machines. Many researchers identified as a key component for scheduling problems, strongly NP-hard. Techniques to solve problems are frequently unfeasible. Metaheuristic techniques more generic and applicable wider range problems. We choose NSGA II in genetic algorithms because they known parallelization, use probabilistic selection techniques, multi-objective evolutionary algorithm enriches the dynamic performance. This analysis sheds light using agents perform decision-making operations. In this paper, we propose implement ANSGA which believe is first of its kind, an agent-based hybrid model provides Pareto front solutions whose individual will satisfy multi-objectives. multi-agent-based approach fastens Computational experiments results our proposed thrive toward optimality, scheduling.