Constrained Problem Optimization using Altered Artificial Bee Colony Algorithm
Shailendra Pratap Singh, Divya Kumar · 2017
Constraint optimization is one of the major fields of decision science where variables and solutions are often constrained or restricted to a certain feasible space only. Moreover, it is always not possible to replace constraints by penalization function only. That is why it is hard to ascertain the exact solution of these types of problems. The Artificial Bee Colony (ABC) algorithm is one of the most prevalent Swarm Intelligence based meta-heuristic algorithm. It is established on the basis of food search behavior of swarms. To tackle constraint optimization problems arising mostly from real world, we have purported a novel Altered-Artificial Bee Colony algorithm (A-ABC). The performance analysis of A- ABC algorithm has been done by testing it on ten classical constraint optimization benchmark functions. The simulation results, when compared with other traditional meta-heuristic approaches, are found best on most of the problems and at-least comparable on remaining one.