scholarly journals Transfer function fitting using a continuous Ant Colony Optimization (ACO) algorithm

2015 ◽  
Vol 4 (2) ◽  
pp. 1 ◽  
Author(s):  
A. Reineix ◽  
C. Guiffaut
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Methods ◽  
Combinatorial Problems ◽  
Ant Colony ◽  
Ultra Wideband ◽  
Complex Frequency ◽  
Function Fitting ◽  
Optimization Principle ◽  
Time Domain Electromagnetic ◽  
The Time Domain

An original approach is proposed in order to achieve the  fitting of ultra-wideband complex frequency functions, such  as the complex impedances, by using the so-called ACO  (Ant Colony Optimization) methods. First, we present the  optimization principle of ACO, which originally was  dedicated to the combinatorial problems. Further on, the  extension to the continuous and mixed problems is  explained in more details. The interest in this approach is  proved by its ability to define practical constraints and  objectives, such as minimizing the number of filters used in  the model with respect to a fixed relative error. Finally, the  establishment of the model for the first and second order  filter types illustrates the power of the method and its  interest for the time-domain electromagnetic computation.

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.

Shortest Path Problem Solving Based on Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 17 (1-2) ◽  
pp. 7-17 ◽  
Author(s):  
Mariusz Gła̢bowski ◽  
Bartosz Musznicki ◽  
Przemysław Nowak ◽  
Piotr Zwierzykowski
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Path Problem ◽  
Combinatorial Problems ◽  
Ant Colony ◽  
Optimization Approach ◽  
Algorithmic Optimization ◽  
Network Utilization ◽  
Shortest Route ◽  
Hard Problems

Abstract The Ant Colony Optimization (ACO) metaheuristic is a versatile algorithmic optimization approach based on the observation of the behaviour of ants. As a result of numerous analyses, ACO has been applied to solving various combinatorial problems. The ant colony metaheuristic proves itself to be efficient in solving NP-hard problems, often generating the best solution in the shortest amount of time. However, not enough attention has been paid to ACO as a means of solving problems that have optimal solutions which can be found using other methods. The shortest path problem is undoubtedly one of the aspects of great significance to navigation and telecommunications. It is used, amongst others, for determining the shortest route between two geographical locations, for routing in packet networks, and to balance and optimize network utilization. Thus, this article introduces ShortestPathACO, an Ant Colony Optimization based algorithm designed to find the shortest path in a graph. The algorithm consists of several subproblems that are presented successively. Each subproblem is discussed from many points of view to enable researchers to find the most suitable solutions to the problems they investigate.

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.

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.

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