Comparison Analysis of Various Optimization Algorithms for Classification of Radar Returns from the Ionosphere

Abstract

Machine learning is developing swiftly as an everexpanding field. The development of the same is occurring rapidly and has made many theoretical breakthroughs in recent times. Due to its importance as a part of machine learning, intelligent optimization algorithms are expected to become increasingly. The exponential growth of data volume and the increase in model complexity present increasing challenges for machine learning optimization strategies. Numerous initiatives have been launched to improve machine learning optimization approaches or address optimization-related problems. Future optimization and machine-learning research can be guided by a detailed evaluation and analysis of optimization strategies from a machine-learning perspective. Machine learning uses a variety of optimization strategies, which makes it easier to compare and analyze how well they function in various situations. In this study, we analyze and contrast seven well-known bio-inspired data engineering techniques and their effectiveness. We apply these techniques to the Radar Returns from the Ionosphere data-set and assess the results with a range of assessment metrics.