Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
