Computational Methods in Engineering

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In this paper, a new algorithm for solving the single loop routing problem is presented. The purpose of the single loop routing problem(SLRP) is to find the shortest loop for an automated guided vehicle covering at least one edge of each department of a block layout. First it shown that this problem can be represented as a graph model. Then a meta-heuristic algorithm based on and colony system is developed for ALRP by using the properties of the graph model. Computational results show the efficiency of the proposed algorithm in comparison with other techniques for solving SLRP.

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A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vectors which are memorized. These vectors are normalized gradient vectors that are calculated using the values of the evaluation function and the corresponding values of object variables. The proposed Gradient-based Continuous Ant Colony Optimization (GCACO) method is applied to several benchmark problems and the results are compared and contrasted with other population-based algorithms such as Evolutionary Strategies (ES), Evolutionary Programming (EP), and Genetic Algorithms (GA). The results obtained from GCACO compare satisfactorily with those of other algorithms and in some cases are superior in terms of accuracy and computational demand.

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This paper aims at optimizing the finite isotropic plates with the hexagonal cutout subjected to  plane loading using metaheuristic optimization algorithms. This research uses Differential Evolution Algorithm (DE) and Harmony Search Algorithm (HSA) from the evolutionary algorithm category, Big Bang- Big Crunch Algorithm (BB-BC) from the physics-based algorithm category, and Grey Wolf Optimizer Algorithm (GWO) and Particle Swarm Optimization (PSO) from the SI algorithm category; then the results of these algorithms are compared with each other. The results indicate that the grey wolf optimizer has the complete performance, short solution time and the ability to avoid local optimums. In the analysis of finite isotropic plate, the effective parameters on stress distribution around the  hexagonal cutouts are cutout bluntness, cutout orientation, plate’s aspect ratio, cutout size, and type of loading. In this study, with the assumption of plane stress conditions, the analytical solution of Muskhelishvili’s complex variable method and conformal mapping is utilized. The plate is considered to be finite (the proportion ratio of the  diameter of circle circumscribing to the longest plate side should be more than 0.2), isotropic, and linearly elastic. The finite element method has been used to check the accuracy of the  results. Numerical results are in a  good agreement with those of the present analytical solution. The results show that by selecting the aforementioned parameters properly, less amounts of stress could achieve around the cutout can lead  to an increase in the load-bearing capacity of the structure.

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One of the most important issues in engineering is to find the optimal global points of the functions used. It is not easy to find such a point in some functions due to the reasons such as large number of dimensions or inability to derive them from the function. Also in engineering modeling, we do not have the relationships of many functions, but we can input and output them as a black box. Therefore, the meta-heuristic algorithms are presented.
In this paper, a meta-heuristic algorithm based on the behavior of vortices in fluid physics is presented. Technically, the algorithm is made up of vortices. Each vortex contains some particles. The particles move by the presented rotation matrix. This movement causes the local search. Also by selecting another vortex through the selection algorithm, each vortex attempts to escape the local optima and reach the global optima. The algorithm will explore and exploit the given function using its operators. Another innovation of this paper is the introduction of two new evaluation criteria for optimization algorithms. These two criteria show the behavior and convergence of algorithms along the way to reach the global optimal point or fall into the local optima. The proposed algorithm has been implemented, evaluated and compared with the numerical optimization state of the art algorithms. It was observed that the proposed method was able to achieve better results than most of the other methods in the major of twenty-four standard functions in different dimensions.  (All codes available at http://web.nit.ac.ir/ h.omranpour/.).