Dr. Pijush Samui

FUTURE PERSPECTIVE OF HYBRID ARTIFICIAL INTELLIGENCE

Abstract:

Traditional Artificial Intelligence (AI) techniques does not always produce encouraging  performance. Hence, modification is essential for obtaining better performance. This  modification has been done by using metaheuristic algorithm. Researchers have used various metaheuristic algorithms such as genetic algorithm, particle swarm optimization, grey wolf optimization, harris hawks optimization, equilibrium optimizer, etc. In metaheuristic optimization, best solution can be defined. The primary advantages of metaheuristic algorithms are their versatility and flexibility. They can be modified easily to fit the specific requirements of a particular dataset. The working procedure of hybrid AI is same as AI. In this lecture, various metaheuristic optimization algorithms will be discussed. Various examples will be given to show the working procedures of Hybrid AI models in different problems of engineering. Participants will know the use of MATLAB for development of different Hybrid AI techniques. The practical application of various AI will be discussed in the field of engineering. This article also gives the advantages of various Hybrid AI techniques.