This paper presents the application of an improved ant colony optimization algorithm called mixed integer distributed ant colony optimization to optimize the power flow solution in power grids. The results provided indicate an improvement in the reduction of operational costs in comparison with other optimization algorithms used in optimal power flow studies. The application was realized to optimize power flow in the IEEE 30 and the IEEE 57 bus test cases with the objective of operational cost minimization. The optimal power flow problem described is a non-linear, non-convex, complex and heavily constrained problem.

The fused deposition modeling process of digital printing uses a layer-by-layer approach to form a three-dimensional structure. Digital printing takes more time to fabricate a 3D model, and the speed varies depending on the type of 3D printer, material, geometric complexity, and process parameters. A shorter path for the extruder can speed up the printing process. However, the time taken for the extruder during printing (deposition) cannot be reduced, but the time taken for the extruder travel (idle move) can be reduced. In this study, the idle travel of the nozzle is optimized using a bioinspired technique called "ant colony optimization" (ACO) by reducing the travel transitions. The ACO algorithm determines the shortest path of the nozzle to reduce travel and generates the tool paths as G-codes. The proposed method’s G-code is implemented and compared with the G-code generated by the commercial slicer, Cura, in terms of build time. Experiments corroborate this finding: the G-code generated by the ACO algorithm accelerates the FDM process by reducing the travel movements of the nozzle, hence reducing the part build time (printing time) and increasing the strength of the printed object.

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances.