ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.

An Improved Nesting Algorithm for Irregular Patterns

In industrial environments, nesting consists in cutting or extracting pieces from a material sheet, with the purpose of minimizing the surface of the sheet used. This problem is present in different types of industries, such as shipping, aeronautics, woodworking, footwear, and so on. In this work, the aim is to find an acceptable solution to solve complex nesting problems. The research developed is oriented to sacrifice accuracy for speed so as to obtain robust solutions in less computational time. To achieve this, a greedy method and a genetic algorithm have been implemented, being the latter responsible for generating a sequence for the placement of the pieces, where each piece is placed in its current optimal position with the help of a representation system for both the pieces and the material sheet.

Nesting Algorithm for Optimization Part Placement in Additive Manufacturing

Significant savings in cost and time can be achieved in additive processes by manufacturing multiple parts in a single setup to obtain efficient machine volume utilization.In this paper the authors have developed a previsional model able to evaluate the potential performance of various printing technologies for the execution of a given job.This model aims to support technicians in choosing the best solution starting from a specific machine architecture and printing volume. In particular, the model is able to evaluate, from a qualitative and quantitative point of view, the performance of each technology in a transversal manner taking into consideration the aspects connected to printing: costs, time and technological parameters.Within the core of the previsional model there are multiple algorithms able to compute different Key Performance Indicator (nine KPIs). For the computation of some of them it was necessary to quantitatively evaluate aspects related to nesting operations, or to the arrangement of several components within the printing base depending on the dimensional characteristics of the component, the printing direction and its dimensional and geometric characteristics (rectangular or circular). Starting from this need, the developed nesting algorithm has given a specific answer.

Descending Packing Algorithm for Irregular Graphics Based on Geometric Feature Points

The packing for two-dimensional irregular graphics is one of the NP-complete problems and widely used in industrial applications. In this paper, a descending nesting algorithm for a two-dimensional irregular graph based on geometric feature points is proposed. Before the packing, the parts to be packed are sorted, matched, and spliced, and the matching of the rectangular pieces and the rectangular-like pieces is carried out according to the plate size. On this basis, the geometric feature points of the parts are used to construct the packing baseline, and the packing is accurately carried out according to the principles of the bottom left, the principle of the lowest center of gravity, and combination with virtual moving, rotating collision calculation. The computation of the moving collision distance between the graphics is replaced by the projecting computation of the geometric feature points of the graphic parts, so the computation amount can be reduced. Also, this method is used to test a number of benchmarks examples which are provided by ESICUP (EURO Special Interest Group on Cutting and Packing), which show that the proposed algorithm not only can improve packing but also has better stability and reliability.

Vertically nested LES for high-resolution simulation of the surface layer in PALM (version 5.0)

Large-eddy simulation (LES) has become a well-established tool in the atmospheric boundary layer research community to study turbulence. It allows three-dimensional realizations of the turbulent fields, which large-scale models and most experimental studies cannot yield. To resolve the largest eddies in the mixed layer, a moderate grid resolution in the range of 10 to 100 m is often sufficient, and these simulations can be run on a computing cluster with a few hundred processors or even on a workstation for simple configurations. The desired resolution is usually limited by the computational resources. However, to compare with tower measurements of turbulence and exchange fluxes in the surface layer, a much higher resolution is required. In spite of the growth in computational power, a high-resolution LES of the surface layer is often not feasible: to fully resolve the energy-containing eddies near the surface, a grid spacing of O(1 m) is required. One way to tackle this problem is to employ a vertical grid nesting technique, in which the surface is simulated at the necessary fine grid resolution, and it is coupled with a standard, coarse, LES that resolves the turbulence in the whole boundary layer. We modified the LES model PALM (Parallelized Large-eddy simulation Model) and implemented a two-way nesting technique, with coupling in both directions between the coarse and the fine grid. The coupling algorithm has to ensure correct boundary conditions for the fine grid. Our nesting algorithm is realized by modifying the standard third-order Runge–Kutta time stepping to allow communication of data between the two grids. The two grids are concurrently advanced in time while ensuring that the sum of resolved and sub-grid-scale kinetic energy is conserved. We design a validation test and show that the temporally averaged profiles from the fine grid agree well compared to the reference simulation with high resolution in the entire domain. The overall performance and scalability of the nesting algorithm is found to be satisfactory. Our nesting results in more than 80 % savings in computational power for 5 times higher resolution in each direction in the surface layer.

Vertically nested LES for high-resolution simulation of the surface layer in PALM (version 5.0)

Large-eddy simulation (LES) has become a well-established tool in the atmospheric boundary-layer research community to study turbulence. It allows three-dimensional realizations of the turbulent fields, which large-scale models and most experimental studies cannot yield. To resolve the largest eddies in the mixed layer, a moderate grid resolution in the range of 10 to 100 m is often sufficient, and these simulations can be run on a computing cluster with few hundred processors, or even on a workstation for simple configurations. The desired resolution is usually limited by the computational resources. However, to compare with tower measurements of turbulence and exchange fluxes in the surface layer a much higher resolution is required. In spite of the growth in computational power, a high-resolution simulation LES of the surface layer is often not feasible: to fully resolve the energy containing eddies near the surface a grid spacing of O(1 m) is required. One way to tackle this problem is to employ a vertical grid nesting technique, where the surface is simulated at the necessary fine grid resolution, and it is coupled with a standard, coarse, LES that resolves the turbulence in the whole boundary-layer. We modified the LES model PALM (Parallelized Large-eddy simulation Model) and implemented a two-way nesting technique, with coupling in both directions between the coarse and the fine grid. The coupling algorithm has to ensure correct boundary conditions for the fine grid. Our nesting algorithm is realized by modifying the standard third order Runge-Kutta time stepping to allow communication of data between the two grids. The two grids are concurrently advanced in time while ensuring that the sum of resolved and subgrid-scale kinetic energy is conserved. We design a validation test and show that the temporal averaged profiles from the fine grid agree well compared to the reference simulation with high-resolution in the entire domain. The overall performance and scalability of the nesting algorithm is found to be satisfactory. Our nesting results in more than 80 percent savings in computational power for 5 times higher resolution in each direction in the surface layer.