An Efficient Hybrid of Continuous Ant Colony Optimization and Weighted Crossover Genetic Algorithm for Optimal Solution

In real time applications the optimization problems that are hard to solve. To solve these kind of problems the algorithms should be specialized and applicable for large range of problems, or they are more general but rather inefficient. In which Evolutionary Algorithms (EA) are more popular which consist of several search heuristics by imitating some features of natural evolution and the social behavior of species. This heuristics algorithm are developed to solve optimization problem but it effectively fail because of convergence and computation time. To overcome this flaws a novel hybrid evolutionary algorithm as Genetic Algorithm (GA) - Continuous Ant Colony Optimization (CACO) is developed. The weighed crossover operation is introduced in genetic algorithm to select the crossover operator. CACO is utilized as a GA mutation then the GA output is given as an input to the CACO. Then the genetic algorithm undergoes the selection, crossover and it gives the result. Based on the comparative analysis, the performance results show the better efficiency and capabilities in finding the optimum solutions.