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Showing 7 results for Ant Colony Optimization

A. Afshar, H. Abbasi, M. R. Jalali,
Volume 4, Issue 1 (3-2006)
Abstract

Water conveyance systems (WCSs) are costly infrastructures in terms of materials, construction, maintenance and energy requirements. Much attention has been given to the application of optimization methods to minimize the costs associated with such infrastructures. Historically, traditional optimization techniques have been used, such as linear and non-linear programming. In this paper, application of ant colony optimization (ACO) algorithm in the design of a water supply pipeline system is presented. Ant colony optimization algorithms, which are based on foraging behavior of ants, is successfully applied to optimize this problem. A computer model is developed that can receive pumping stations at any possible or predefined locations and optimize their specifications. As any direct search method, the mothel is highly sensitive to setup parameters, hence fine tuning of the parameters is recommended.
M.h. Afshar, H. Ketabchi, E. Rasa,
Volume 4, Issue 4 (12-2006)
Abstract

In this paper, a new Continuous Ant Colony Optimization (CACO) algorithm is proposed for optimal reservoir operation. The paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. The paper also proposes an elitist strategy for CACO algorithm where best solution of each iteration is directly copied to the next iteration to improve performance of the method. The performance of the CACO algorithm is demonstrated against some benchmark test functions and compared with some other popular heuristic algorithms. The results indicated good performance of the proposed method for global minimization of continuous test functions. The method was also used to find the optimal operation of the Dez reservoir in southern Iran, a problem in the reservoir operation discipline. A normalized squared deviation of the releases from the required demands is considered as the fitness function and the results are presented and compared with the solution obtained by Non Linear Programming (NLP) and Discrete Ant Colony Optimization (DACO) models. It is observed that the results obtained from CACO algorithm are superior to those obtained from NLP and DACO models.
Hon.m. Asce, M.r. Jalali, A. Afshar, M.a. Mariño,
Volume 5, Issue 4 (12-2007)
Abstract

Through a collection of cooperative agents called ants, the near optimal solution to the multi-reservoir operation problem may be effectively achieved employing Ant Colony Optimization Algorithms (ACOAs). The problem is approached by considering a finite operating horizon, classifying the possible releases from the reservoir(s) into pre-determined intervals, and projecting the problem on a graph. By defining an optimality criterion, the combination of desirable releases from the reservoirs or operating policy is determined. To minimize the possibility of premature convergence to a local optimum, a combination of Pheromone Re-Initiation (PRI) and Partial Path Replacement (PPR) mechanisms are presented and their effects have been tested in a benchmark, nonlinear, and multimodal mathematical function. The finalized model is then applied to develop an optimum operating policy for a single reservoir and a benchmark four-reservoir operation problem. Integration of these mechanisms improves the final result, as well as initial and final rate of convergence. In the benchmark Ackley function minimization problem, after 410 iterations, PRI mechanism improved the final solution by 97 percent and the combination of PRI and PPR mechanisms reduced final result to global optimum. As expected in the single-reservoir problem, with a continuous search space, a nonlinear programming (NLP) approach performed better than ACOAs employing a discretized search space on the decision variable (reservoir release). As the complexity of the system increases, the definition of an appropriate heuristic function becomes more and more difficult this may provide wrong initial sight or vision to the ants. By assigning a minimum weight to the exploitation term in a transition rule, the best result is obtained. In a benchmark 4-reservoir problem, a very low standard deviation is achieved for 10 different runs and it is considered as an indication of low diversity of the results. In 2 out of 10 runs, the global optimal solution is obtained, where in the other 8 runs results are as close as 99.8 percent of the global solution. Results and execution time compare well with those of well developed genetic algorithms (GAs).
A. Kaveh, N. Farhoodi,
Volume 8, Issue 3 (9-2010)
Abstract

In this paper, the problem of layout optimization for X-bracing of steel frames is studied using the ant system (AS). A new design method is employed to share the gravity and the lateral loads between the main frame and the bracings according to the requirements of the IBC2006 code. An algorithm is developed which is called optimum steel designer (OSD). An optimization method based on an approximate analysis is also developed for layout optimization of braced frames. This method is called the approximate optimum steel designer (AOSD) and uses a simple deterministic optimization algorithm leading to the optimum patterns and it is much faster than the OSD. Several numerical examples are treated by the proposed methods. Efficiency and accuracy of the methods are then discussed. A comparison is also made with Genetic algorithm for one of the frames.


Ali Kaveh, Omid Sabzi,
Volume 9, Issue 3 (9-2011)
Abstract

This article presents the application of two algorithms: heuristic big bang-big crunch (HBB-BC) and a heuristic particle swarm

ant colony optimization (HPSACO) to discrete optimization of reinforced concrete planar frames subject to combinations of

gravity and lateral loads based on ACI 318-08 code. The objective function is the total cost of the frame which includes the cost

of concrete, formwork and reinforcing steel for all members of the frame. The heuristic big bang-big crunch (HBB-BC) is based

on BB-BC and a harmony search (HS) scheme to deal with the variable constraints. The HPSACO algorithm is a combination of

particle swarm with passive congregation (PSOPC), ant colony optimization (ACO), and harmony search scheme (HS)

algorithms. In this paper, by using the capacity of BB-BC in ACO stage of HPSACO, its performance is improved. Some design

examples are tested using these methods and the results are compared.


A. Kaveh, A. Nasrolahi,
Volume 12, Issue 1 (3-2014)
Abstract

In this paper, a new enhanced version of the Particle Swarm Optimization (PSO) is presented. An important modification is made by adding probabilistic functions into PSO, and it is named Probabilistic Particle Swarm Optimization (PPSO). Since the variation of the velocity of particles in PSO constitutes its search engine, it should provide two phases of optimization process which are: exploration and exploitation. However, this aim is unachievable due to the lack of balanced particles’ velocity formula in the PSO. The main feature presented in the study is the introduction of a probabilistic scheme for updating the velocity of each particle. The Probabilistic Particle Swarm Optimization (PPSO) formulation thus developed allows us to find the best sequence of the exploration and exploitation phases entailed by the optimization search process. The validity of the present approach is demonstrated by solving three classical sizing optimization problems of spatial truss structures.
Mohsen Shahrouzi, Amir Abbas Rahemi,
Volume 12, Issue 2 (6-2014)
Abstract

Well-known seismic design codes have offered an alternative equivalent static procedure for practical purposes instead of verifying design trials with complicated step-y-step dynamic analyses. Such a pattern of base-shear distribution over the building height will enforce its special stiffness and strength distribution which is not necessarily best suited for seismic design. The present study, utilizes a hybrid optimization procedure to seek for the best stiffness distribution in moment-resistant building frames. Both continuous loading pattern and discrete sizing variables are treated as optimization design variables. The continuous part is sampled by Harmony Search algorithm while a variant of Ant Colony Optimization is utilized for the discrete part. Further search intensification is provided by Branch and Bound technique. In order to verify the design candidates, static, modal and time-history analyses are applied regarding the code-specific design spectra. Treating a number of building moment-frame examples, such a hyper optimization resulted in new lateral loading patterns different from that used in common code practice. It was verified that designing the moment frames due to the proposed loading pattern can result in more uniform story drifts. In addition, locations of the first failure of columns were transmitted to the upper/less-critical stories of the frame. This achievement is important to avoid progressive collapse under earthquake excitation.

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