Ant Colony Optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperates in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to Max-Min Ant System structure and exploits a local search heuristic to improve its performance. Experimental resu...

The well-known Ant Colony Optimization method is applied to the problem of tuning the parameters of a Takagi-Sugeno fuzzy rule base. The latest developed Continuous Ant Colony System algorithm is used to find the global solution of the continuous optimization problem. The method is systematically tested for the approximation of one analytical test function and the performance of the proposed sc...

Total weighted tardiness is a measure of customer satisfaction. Minimizing it represents satisfying the general requirement of on-time delivery. In this research, we consider an ant colony optimization (ACO) algorithm to solve the problem of scheduling unrelated parallel machines to minimize total weighted tardiness. The problem is NP-hard in the strong sense. Computational results show that th...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...

This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machin...

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based...

The Vehicle Routing Problem (VRP) is an important management problem in the field of physical distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make logistics management more scientific. The Capacitated Vehicle Routing Problem (CVRP) constrained by the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm ...