ACM Transactions on Mathematical Software (TOMS)
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Parallel ant colonies for the quadratic assignment problem
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Ant colony optimization-based algorithm for airline crew scheduling problem
Expert Systems with Applications: An International Journal
Gaussian variable neighborhood search for continuous optimization
Computers and Operations Research
A memetic particle swarm optimization algorithm for multimodal optimization problems
Information Sciences: an International Journal
Global optimization using a genetic algorithm with hierarchically structured population
Journal of Computational and Applied Mathematics
Hi-index | 12.06 |
The ant colony optimization (ACO) algorithms, which are inspired by the behaviour of ants to find solutions to combinatorial optimization problem, are multi-agent systems. This paper presents the ACO-based algorithm that is used to find the global minimum of a nonconvex function. The algorithm is based on that each ant searches only around the best solution of the previous iteration. This algorithm was tested on some standard test functions, and successful results were obtained. Its performance was compared with the other algorithms, and was observed to be better.