Hybrid Crossover - Mutation Pair for Genetic Algorithm in Solving Fuzzy Shortest Path Problem - Predominant and Subordinate Ants

The reasons behind the evolution of fuzzy shortest path problem are, finding the path of least cost from source vertex to the destination vertex in the graph G={V, E}. Fuzzy shortest path problem comprises of fuzzy numbers as parameters and here generalized trapezoidal fuzzy numbers and their characteristics are used. In order to upgrade the optimization, evolutionary optimization is often used and hence Genetic Algorithm (GA) is packed with Ant Colony Optimization (ACO) for the better optimization. Our objective of the research is to hybrid each and every individual genetic operator with ant. In this paper, we took mutation and crossover operators to hybrid, not only for proposed problem and also wherever in the Genetic Algorithm (GA) and network topology combination. The proposed methodology hybrids the characteristics of ants so called predominant and subordinate ants with the conventional operator in which, is a first experiment ever in the history of hybridization with the best of our knowledge. The most used crossover and mutation operators are reviewed and the proposed is compared. The implementation of proposed and conventional methods is carried out in MATLAB and experimental result explains the importance of crossover and mutation operators in genetic algorithm and also the effectiveness of the proposed hybridization in the convergence and time complexity of the algorithm.