A Hybrid CGJO Algorithm for Optimizing Multimodal and Fixed Benchmark Functions

Electrical Engineering

Artificial Intelligence (AI) plays a significant role in the use of AI to find its solution in complex optimization challenges. All such challenges are for example selecting from a set of alternatives under restrictions. AI blends the adaptive, efficient, and scalable methodologies that yield an application of a meaningful improvement over traditional optimization approaches. Further, Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) are AI driven optimization procedure and have been used to solve complicated, multiple objectives, dynamic problem. The techniques are all inspired by natural phenomena, such as evolution, collective swarm behavior, and a neural learning process, all good methods of going through an exciting solution space to hit close to optimal results. In all the walks of life including engineering, finance, healthcare, logistics and more, they are highly versatile because through them we determine the best possible action to be taken in decision making and withdrawal of resources.

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