A Analyzing and Implementing Metaheuristic GWO and FF Algorithms aiming to develop a Fault-Tolerant Hybrid GWOFF Algorithm introduction, state of art, contribution and implementation, conclusion and future work, references

Abstract

The approach adopted to handle hard problems is known as metaheuristics. The problem is considered as hard if discovering the optimal solution for it may not be always possible within the stipulated time. Discovering a single solution to a problem is easy and can be accomplished extremely fast, but finding the best possible solution to the same problem is very long. Optimization algorithms are intended to bridge this gap. The research paper aims at solving the problems for finding the optimal solution for two popular metaheuristic algorithms, GWO (Grey Wolf Optimization) and FF (Firefly) algorithms. Both the metaheuristics algorithms, GWO and FF algorithms are studied and implemented. The two technical features comprised of metaheuristic algorithms are exploration and exploitation. The optimal solution has been evaluated alongside Makespan and Utilization Rate for both GWO and FF algorithms. The lower value of the Makespan and higher Utilization Rate is always desirable. Both the algorithms have been modified via using mathematical functions to enhance the readings concerning performance evaluation parameters. The GWO is been modified via developing a hybrid version comprising GWO and PSO (Particle Swarm Optimization) algorithms denoted as the Hybrid Modified GWOPSO algorithm. The FF algorithm too has been modified and is denoted as a Modified FF algorithm. The conducted modifications have been measured via different performance evaluation parameters. Finally, the fault tolerance factor is considered and the modified versions Hybrid Modified GWOPSO and Modified FF are hybridized to develop a new hybrid algorithm Hybrid GWOFF (Hybrid Grey Wolf Firefly) algorithm and its performance have been evaluated with and without fault tolerance.