An improved Evaporation Rate-Water Cycle Algorithm based Genetic Algorithm for solving generalized ratio problems

This paper presents an efficient metaheuristic approach for optimizing the generalized ratio problems such as sum and multiplicative of linear or nonlinear objective function with affine constraints. focuses on significance hybrid techniques, which are implemented by using GA ER-WCA to increase efficiency robustness solving problems. Initially, starts initial random population it is processed genetic operators. will observe preserve GAs fittest chromosome in each cycle every generation. Genetic algorithm provided better optimal solutions while constrained optimization Also, effectiveness proposed analyzed large scale The results performance ensures a strong improves exploitative process when compared other existing techniques. Numerical applications used test convergence accuracy approached method. behavior this those evolutionary algorithms namely Neural Network Algorithm, Grey Wolf Optimization, Evaporation Rate - Water Cycle Firefly algorithm, Cuckoo search algorithm. evaluated show that increases more than algorithms.