Exploration and Comparative Implementation of Hybrid GWOPSO Algorithm against GWO

Whenever humans have tried to explore and overcome nature, a lot of glitches with gigantic complexities having no investigative solutions have been encountered. It has always been hard to find a universal solution to all the problems. With the snowballing prerequisite of exhausting computing resources as a non-scale bounded substructure, the swing towards cloud computing has witnessed an exponential increase to decrease the total cost. The developers make use of shared resources in a cloud environment. Exceptional algorithms are required to map resources with the demands of the users. To offer users effective solutions, more complex efficient algorithms are needed. GWO (Grey Wolf Optimizer) is one of the listed metaheuristic methods together with PSO (Particle Swarm Optimization). The research paper is one such attempt to enhance the performance of the GWO algorithm via proposing a hybrid GWOPSO algorithm comprising the effective features of both GWO and PSO algorithms. The research paper discusses the leadership hierarchy of grey wolves adopted to hunt the prey finding the best optimal solution. The research paper elaborates on the working principle of both GWO and hybrid GWOPSO algorithm via a mathematical application, flowcharts, and algorithms. The primary objective of the proposed research is to minimize the execution time and optimize the use of available resources to obtain an optimal solution. The implementation has been conducted to record the values of performance evaluation parameters like waiting time, turnaround time, throughput, the best solution, and best optimal values and it has been found that the proposed hybrid PSOGWO algorithms scores over the GWO algorithm.