A Comprehensive Study on Learning Strategies of Optimization Algorithms and its Applications

Abstract

Optimization (or) Heuristic problems are termed as NP-hard problem. The algorithms used to estimate the heuristic value like Hill Climbing and Random Search have more impact on finding out the solution close to optimal solution. The problem identified in such algorithm is over estimation (Global optima) and under estimation (Local optima) of heuristic value. In both the cases, the search operation will not produce optimal solution. In the present work, the research carried out on optimization algorithms from 2017 to 2020 is presented along with applications and the limitations. The objective of the research work is to make the readers to understand the impact of Time and Space complexity of such optimization algorithms and their performance in implementing different learning strategies towards finding out the optimal solution. The research carried out in the present work, will help in providing the direction to the researcher doing research in the area of optimization algorithm and its applications.