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Full-Text Articles in Systems Engineering
The Tabu Ant Colony Optimizer And Its Application In An Energy Market, David Donald Haynes
The Tabu Ant Colony Optimizer And Its Application In An Energy Market, David Donald Haynes
Doctoral Dissertations
"A new ant colony optimizer, the 'tabu ant colony optimizer' (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the …
Simulation And Optimization Of Ant Colony Optimization Algorithm For The Stochiastic Uncapacitated Location-Allocation Problem, Jean-Paul Arnaout, Georges Arnaout, John El Khoury
Simulation And Optimization Of Ant Colony Optimization Algorithm For The Stochiastic Uncapacitated Location-Allocation Problem, Jean-Paul Arnaout, Georges Arnaout, John El Khoury
Engineering Management & Systems Engineering Faculty Publications
This study proposes a novel methodology towards using ant colony optimization (ACO) with stochastic demand. In particular, an optimizationsimulation-optimization approach is used to solve the Stochastic uncapacitated location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. ACO is modeled using discrete event simulation to capture the randomness of customers’ demand, and its objective is to optimize the costs. On the other hand, the simulated ACO’s parameters are also optimized to guarantee superior solutions. This approach’s performance is evaluated by comparing its solutions to the ones obtained using deterministic data. The results …
Wolf Ant, Gilbert L. Peterson, Christopher M. Mayer, Kevin Cousin
Wolf Ant, Gilbert L. Peterson, Christopher M. Mayer, Kevin Cousin
Faculty Publications
Ant colony optimization (ACO) algorithms can generate quality solutions to combinatorial optimization problems. However, like many stochastic algorithms, the quality of solutions worsen as problem sizes grow. In an effort to increase performance, we added the variable step size off-policy hill-climbing algorithm called PDWoLF (Policy Dynamics Win or Learn Fast) to several ant colony algorithms: Ant System, Ant Colony System, Elitist-Ant System, Rank-based Ant System, and Max-Min Ant System. Easily integrated into each ACO algorithm, the PDWoLF component maintains a set of policies separate from the ant colony's pheromone. Similar to pheromone but with different update rules, the PDWoLF policies …