A Hybrid Optimization Algorithm for Pathfinding in Grid Environment

Grid computing has been highly effective in the area of life sciences, financial analysis, research collaboration, and engineering. This paper is a study of existing algorithms like Swarm Intelligence (SI) algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC–PSO), and Parallel Particle Swarm Optimization (PPSO) to opt for the optimal path in a grid computing environment. These algorithms were used to solve the complex optimization problems in finding the path between source node to destination node effectively. Nature computing techniques based on the study of the collective behavior of ants, particle swarms, and bees are used to find the optimal path, improve the optimization methods and scalability in a set of representative problems. The hybridization of a grid computing environment and nature-inspired computing algorithms such as ACO, PSO, ABC–PSO, and PPSO has resulted in a class of solutions that differ in structure and design from the peer-to-peer network algorithms and the evaluated results showed the effectiveness of the pathfinding problem. ACO is implemented on a dynamic grid computing environment to demonstrate scalability and a solution for pathfinding. A class of four algorithms is used to find an optimal path and improve the optimization methods and shorten the computational time in a grid computing environment.