Improve the accuracy and convergence of the FPA algorithm by using hybrid algorithms

Inspired by nature and the laws that govern it, it has solved many unknowns and problems around us. The fascinating behavior of animals, plants, magnifying glass creatures, and even physical phenomena has led to the development of an intelligent search algorithm called evolutionary methods. Evolutionary algorithms, unlike conventional methods of solving classical problems such as mathematics and numerical calculations, emphasize random and natural phenomena around us to solve optimization problems. Optimization issues can be seen in all sciences, especially computer science and industry. An optimization problem is a model of an applied problem that has several solutions that researchers are interested in choosing the least expensive ones to implement. Evolutionary methods, unlike traditional methods, which are limited to solving a certain number of optimization problems, are able to solve a wide range of such problems with appropriate and high accuracy. In this paper, with the help of evaluation functions that are used to measure the efficiency of evolutionary algorithms. Evaluate and compare the standard version of the flower pollination algorithm and the improved version using the genetic algorithm to determine their efficiency and accuracy. In this chapter, some case studies will be described graphically along with the average results. The purpose of this chapter is to analyze the proposed algorithm and compare it with the standard flower dusting algorithm.

Keywords:

Improved accuracy , algorithm convergence , hybrid algorithms.