RGBCA-genetic bee colony algorithm for travelling salesman problem
Vikas Pratap Singh, Ritu Tiwari, Deepak Singh, Anupam Shukla · 2011
Challenge of finding the shortest route visiting each member of a collection of locations and returning to starting point is an NP-hard problem. It is also known as Traveling salesman problem, TSP is specific problem of combinatorial optimization studied in computer science and mathematical applications. In our work we present a hybrid version of Evolutionary algorithm to solve TSP problem. In this method we extend Genetic Algorithm with Artificial Bee Colony operators i:e Employed Bees and Onlooker Bees to improve the solution space named as Real Genetic Bee Colony Algorithm (RGBCA). Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution. Artificial Bee Colony (ABC) is an optimization algorithm based on the intelligent behavior of honey bee swarm. In the proposed method we extend GA with two operators of ABC for local search strategy. In this hybrid procedure (RGBCA), the exploitation process in the ABC algorithm improves classical Genetic Algorithm. The experimental results show that compared to original GA, our GBCA model can reach broader domains in the search space and show improvements in both precision and computational time.