Comprehensive Study of Population Based Algorithms

The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates and high fitness scores. The investigation examines each algorithm's unique features, optimization properties, and operational paradigms, conducting broad comparative analyses against conventional methods, such as search history, fitness functions and to express their superiority. The study also assesses their relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. The findings show the transformative potential of these algorithms and offering valuable wisdom for future research to enhance and broaden upon these methodologies. This finding assists as a guiding for researchers to enable inventive solutions based in natural algorithms and advancing the field of optimization.