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

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