Performance Exploration on Some Neoteric Meta-heuristic-Based Approaches for Problem Solving in Optimization

Ever since the early 1960s, several swarm optimization techniques have been developed, ranging from the most recent to dynamic algorithms. Each of the above techniques is effective at tackling various nonlinear equations. In the present manuscript, we have presented an effective exploration on various optimization algorithms mainly Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grasshopper Optimization Algorithm (GOA), and Cuckoo Search (CS) for understanding their working and problems associated with them. In the majority of the optimization algorithms, there is a chance for updation due to either having a slow convergence rate or getting trapped in local minima or both that degrade the performance of the algorithm. We have done the analysis of existing algorithms based on the parameters like Computational Time, Memory Usage, Parallelization Capability, and Handling Constraints. Several researchers have implemented various techniques for optimizing their performance. Analysis of algorithms has also been discussed in this manuscript for clear understanding of the existing work.