Optimum buckling design of composite stiffened panels using ant colony algorithm

Optimal design of laminated composite stiffened panels of symmetric and balanced layup with different number of T-shape stiffeners is investigated and presented. The stiffened panels are simply supported and subjected to uniform biaxial compressive load. In the optimization for the maximum buckling load without weight penalty, the panel skin and the stiffened laminate stacking sequence, thickness and the height of the stiffeners are chosen as design variables. The optimization is carried out by applying an ant colony algorithm (ACA) with the ply contiguous constraint taken into account. The finite strip method is employed in the buckling analysis of the stiffened panels. The results shows that the buckling load increases dramatically with the number of stiffeners at first, and then has only a small improvement after the number of stiffeners reaches a certain value. An optimal layup of the skin and stiffener laminate has also been obtained by using the ACA. The methods presented in this paper should be applicable to the design of stiffened composite panels in similar loading conditions

A Study Of Vantage Point Neighbourhood Search In The Bees Algorithm For Combinatorial Optimization Problems

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc. ) -- İstanbul Technical University, Institute of Science and Technology, 2014Bu tez çalışmasının temel amacı arıların kaynak arama davranışlarını modelleyen arı algoritmasının, kombinatoryal uzaylarda komşuluk arama fazına yeni bir yaklaşım geliştirilmesidir. Geliştirilen yaklaşım Gezgin Satıcı Problemine uygulanarak Gezgin Satıcı Problemi çözümünün en iyilenmesi amaçlanmıştır.This thesis focuses on nature-inspired optimisation algorithms, in particular, the Bees Algorithm that developed for combinatorial domains with new local search procedure and applied to Traveller Salesman Problem (TSP). An efficient and robust local neighborhood search algorithm is proposed for combinatorial domains to increase the efficiency of the Bees Algorithm.Yüksek LisansM.Sc

Orthogonal methods based ant colony search for solving continuous optimization problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others

Traveling Salesman Problem

The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance

Path planning for general mazes

Path planning is used in, but not limited to robotics, telemetry, aerospace, and medical applications. The goal of the path planning is to identify a route from an origination point to a destination point while avoiding obstacles. This path might not always be the shortest in distance as time, terrain, speed limits, and many other factors can affect the optimality of the path. However, in this thesis, the length, computational time, and the smoothness of the path are the only constraints that will be considered with the length of the path being the most important. There are a variety of algorithms that can be used for path planning but Ant Colony Optimization (ACO), Neural Network, and A* will be the only algorithms explored in this thesis. The problem of solving general mazes has been greatly researched, but the contributions of this thesis extended Ant Colony Optimization to path planning for mazes, created a new landscape for the Neural Network to use, and added a bird\u27s eye view to the A* Algorithm. The Ant Colony Optimization that was used in this thesis was able to discover a path to the goal, but it was jagged and required a larger computational time compared to the Neural Network and A* algorithm discussed in this thesis. The Hopfield-type neural network used in this thesis propagated energy to create a landscape and used gradient decent to find the shortest path in terms of distance, but this thesis modified how the landscape was created to prevent the neural network from getting trapped in local minimas. The last contribution was applying a bird\u27s eye view to the A* algorithm to learn more about the environment which helped to create shorter and smoother paths

Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets