scholarly journals Determining the Optimum Section of Tunnels Using Ant Colony Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
S. Talatahari
Keyword(s):  
Objective Function ◽  
Ant Colony Optimization ◽  
Cross Sections ◽  
Optimization Methods ◽  
Ant Colony ◽  
Great Role ◽  
Numerical Examples ◽  
First Time

Ant colony optimization is developed to determine optimum cross sections of tunnel structures. Tunnel structures are expensive infrastructures in terms of material, construction, and maintenance and the application of optimization methods has a great role in minimizing their costs. This paper presents the formulation of objective function and constraints of the problem for the first time, and the ant colony optimization, as a developed metaheuristic approach, has been used to solve the problem. The results and comparisons based on numerical examples show the efficiency of the algorithm.

Risk Budgeted Portfolio Optimization Using an Extended Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 3 (4) ◽  
pp. 25-42 ◽  
Author(s):  
G. A. Vijayalakshmi Pai
Keyword(s):  
Ant Colony Optimization ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Experimental Studies ◽  
Optimization Methods ◽  
Investment Strategy ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Exchange Data

Risk Budgeted portfolio optimization problem centering on the twin objectives of maximizing expected portfolio return and minimizing portfolio risk and incorporating the risk budgeting investment strategy, turns complex for direct solving by classical methods triggering the need to look for metaheuristic solutions. This work explores the application of an extended Ant Colony Optimization algorithm that borrows concepts from evolution theory, for the solution of the problem and proceeds to compare the experimental results with those obtained by two other Metaheuristic optimization methods belonging to two different genres viz., Evolution Strategy with Hall of Fame and Differential Evolution, obtained in an earlier investigation. The experimental studies have been undertaken over Bombay Stock Exchange data set (BSE200: July 2001-July 2006) and Tokyo Stock Exchange data set (Nikkei225: July 2001-July 2006). Data Envelopment Analysis has also been undertaken to compare the performance of the technical efficiencies of the optimal risk budgeted portfolios obtained by the three approaches.

scholarly journals GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

2021 ◽  
Vol 7 ◽  
pp. e729
Author(s):  
Mulki Indana Zulfa ◽  
Rudy Hartanto ◽  
Adhistya Erna Permanasari ◽  
Waleed Ali
Keyword(s):  
Objective Function ◽  
Ant Colony Optimization ◽  
Data Exchange ◽  
Edge Computing ◽  
Ant Colony ◽  
Solution Quality ◽  
Selection Probability ◽  
Time Consumption ◽  
Solution Selection ◽  
Data Optimization

Background Data exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time. Methods We proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results. Result The experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

scholarly journals Complete Inverse Method Using Ant Colony Optimization Algorithm for Structural Parameters and Excitation Identification from Output Only Measurements

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Jun Chen ◽  
Xin Chen ◽  
Wei Liu
Keyword(s):  
Structural Health Monitoring ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Health Monitoring ◽  
Inverse Method ◽  
Structural Parameters ◽  
Ant Colony ◽  
Computation Method ◽  
Structural Health ◽  
Output Only

In vibration-based structural health monitoring of existing large civil structures, it is difficult, sometimes even impossible, to measure the actual excitation applied to structures. Therefore, an identification method using output-only measurements is crucial for the practical application of structural health monitoring. This paper integrates the ant colony optimization (ACO) algorithm into the framework of the complete inverse method to simultaneously identify unknown structural parameters and input time history using output-only measurements. The complete inverse method, which was previously suggested by the authors, converts physical or spatial information of the unknown input into the objective function of an optimization problem that can be solved by the ACO algorithm. ACO is a newly developed swarm computation method that has a very good performance in solving complex global continuous optimization problems. The principles and implementation procedure of the ACO algorithm are first introduced followed by an introduction of the framework of the complete inverse method. Construction of the objective function is then described in detail with an emphasis on the common situation wherein a limited number of actuators are installed on some key locations of the structure. Applicability and feasibility of the proposed method were validated by numerical examples and experimental results from a three-story building model.

scholarly journals Efficient multi-objective optimization methods for computationally intensive urban water resources models

2014 ◽  
Vol 17 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Mohammad Mortazavi-Naeini ◽  
George Kuczera ◽  
Lijie Cui
Keyword(s):  
Ant Colony Optimization ◽  
Water Resource ◽  
Optimization Methods ◽  
Optimization Method ◽  
Ant Colony ◽  
Urban Water ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multi-objective optimization methods require many thousands of objective function evaluations. For urban water resource problems such evaluations can be computationally very expensive. The question as to which optimization method is the best choice for a given function evaluations budget in urban water resource problems remains unexplored. The main objective of this paper is to address this question. The second objective is to develop a new optimization algorithm, efficient multi-objective ant colony optimization-I (EMOACO-I), which exploits the good performance of ant colony optimization enhanced using ideas borrowed from evolutionary optimization. Its performance was compared against three established methods (NSGA-II, SMPSO, εMOEA) using two case studies based on the urban water resource systems serving two major Australian cities. The case study problems involved two or three objectives and 10 or 13 decision variables affecting infrastructure investment and system operation. The results show that NSGA-II was the worst performing method. However, none of the remaining methods was unambiguously superior. For example, while EMOACO-I converged more rapidly, its diversity was comparable but not superior to the other methods. Greater differences in performance were found as the number of objectives and case study complexity increased. This suggests that pooling the results from a number of methods could help guard against the vagaries in performance of individual methods.

Hunting Search and Ant Colony Optimization Mechanisms on Modified Simplex Method for Grease Position Parameter Design

2012 ◽  
Vol 217-219 ◽  
pp. 1471-1474
Author(s):  
Suphatchai Boonhao ◽  
Pongchanun Luangpaiboon
Keyword(s):  
Ant Colony Optimization ◽  
Simplex Method ◽  
Optimization Methods ◽  
Ant Colony ◽  
Dual Process ◽  
Parameter Design ◽  
Design Parameters ◽  
Actual Performance ◽  
Response Surface Optimization ◽  
The Mean

The proper choices of a process parameter design (PPD) decision problem are studied in a noisy environment of a grease position process in an electronic industry. The process responses consist of the mean of parts between failures on left and right processes. A multi-response surface optimization problem is then proposed to maximize these dual process responses. The conventional modified simplex method and its hybridizations of the stochastic operators from the hunting search and ant colony optimization methods are applied to determine the proper levels of controllable design parameters affecting the quality performances. A numerical example demonstrates the feasibility of applying the proposed model to the PPD problem via two iterative methods. Its advantages are also discussed. Numerical results demonstrate that the hybridization based on the hunting search method seems to be superior when compared. In this study, the mean of parts between failures is improved by 27.04%, approximately. All experimental data presented in this research have been normalized to disguise actual performance measures as raw data are considered to be confidential.

scholarly journals Adapting ant colony optimization of compliant structures using elements with linearly varying cross-sections

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Nadim Diab ◽  
Farah Jouni
Keyword(s):  
Ant Colony Optimization ◽  
Cross Section ◽  
Cross Sections ◽  
Compliant Mechanism ◽  
Optimization Technique ◽  
Ant Colony ◽  
Internal Stresses ◽  
Output Displacement ◽  
Amplification Ratio ◽  
Stiffness Matrices

AbstractThis work addresses the design of miniature compliant displacement amplifiers. The optimum design of the compliant mechanism is generated through topology optimization of two-node frame elements with linearly varying cross sections using the Ant Colony Optimization technique. First, stiffness matrices that account for the change in the cross-section dimensions are formulated. Then, each element is assigned 5 independent ants that represent its design variables defined as the width and thickness of each of the two peripheral cross-sections in addition to the material density. Three case studies with customized cost functions are furnished; the first maximizes the amplification ratio, the second maximizes the output displacement, while the third maximizes both amplification ratio and output displacement simultaneously. The resulting micro-compliant amplifiers are more compact in volume and surpass their constant cross-section counterparts in terms of amplification ratio and output displacement while keeping relatively low internal stresses. The performances of all optimized topologies are verified through ANSYS.

Optimization Methods Applied to Selecting Support Positions in Fixture Design

1991 ◽  
Vol 113 (4) ◽  
pp. 412-418 ◽  
Author(s):  
R. J. Menassa ◽  
W. R. DeVries
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Objective Function ◽  
Design Problem ◽  
Optimization Methods ◽  
Design Criterion ◽  
Optimization Techniques ◽  
Fixture Design ◽  
The Finite Element Method ◽  
Numerical Examples

This paper proposes optimization techniques to assist in the design and evaluation of fixtures for holding prismatic workpieces. This formulation of the fixturing design problem takes into account deflection of the workpiece subjected to assembly or machining loads. Using the minimization of the workpiece deflection at selected points as the design criterion, the design problem is determining the positions of the fixture supports. The Finite Element Method is used for calculating deflections that are the basis for the design objective function, and the Broyden-Fletcher-Goldfarb-Shanno optimization algorithm is used to determine the fixture support positions. In this paper the proposed objective function is developed and the method is illustrated with three numerical examples.

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