A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Ant colonies using arc consistency techniques for the set partitioning problem

In this paper, we solve some benchmarks of Set Covering Problem and Equality Constrained Set Covering or Set Partitioning Problem. The resolution techniques used to solve them were Ant Colony Optimization algorithms and Hybridizations of Ant Colony Optimization with Constraint Programming techniques based on Arc Consistency. The concept of Arc Consistency plays an essential role in constraint satisfaction as a problem simplification operation and as a tree pruning technique during search through the detection of local inconsistencies with the uninstantiated variables. In the proposed hybrid algorithms, we explore the addition of this mechanism in the construction phase of the ants so they can generate only feasible partial solutions. Computational results are presented showing the advantages to use this kind of additional mechanisms to Ant Colony Optimization in strongly constrained problems where pure Ant Algorithms are not successful.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Influence of Programming Language on the Execution Time of Ant Colony Optimization Algorithm

Supply chains can be accelerated by route optimization, a computationally intensive process for a large number of instances. Traveling Salesmen Problem, as the representative example of routing problems, is NP-hard combinatorial problem. It means that the time needed for solving the problem with exact methods increases exponentially with the increased dataset. Using metaheuristic methods, like Ant Colony Optimization, reduces the time needed for solving the problem drastically but finding a solution still takes a considerable amount of time for large datasets. In today’s dynamic environment finding the solution as fast as possible is important as finding a quality solution. The programming language used for finding the solution also influences execution time. In this paper, the execution time of Ant Colony Optimization to solve Traveling Salesman Problems of different sizes was measured. The algorithm was programmed in several programming languages, execution time was measured to rank programming languages

A Heuristic Approach to Locating a Landfill Site in the Sekondi- Takoradi Metropolis of Ghana

The rise in the urban population of developing countries in the past decade has brought the necessity to develop environmentally sustainable and efficient waste management systems. Sanitary landfill constitutes one of the primary methods of municipal solid waste disposal. Optimized siting has gained considerable importance in order to ensure minimum damage to the environment. Despite an increase in alternative techniques for disposing of waste, landfill site still remains the primary means of waste disposal. This study seeks to find the optimal location of a landfill site in the Sekondi -Takoradi metropolis. The problem was formulated as an Integer Programming model and the solution was presented through an ant colony based meta-heuristic for the Travelling Salesman Problem. The data on distances were taken from potential collection points and also Cartesian coordinates of the collection points were collected and used for each sub-metro. The optimal solutions were obtained with the aid of a MATLAB implementation codes. The Ant Colony results revealed that the best location for an additional landfill site is Apremedo with a route length of a 133.35miles. Keywords: Ant colony, Travelling Salesman Problem, Landfill site, Optimization and Integer Programming

Nonpermutation flow line scheduling by ant colony optimization

A flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion

Design of Steel Frames Using an Application Programming Interface with Ant Colony Optimization

Assfoura, Hanan. M.S. Civil Engineeting. The University of Memphis. May/2013. Design of Steel frames Using an Application Programming Interface with Ant Colony Optimization. Charles Camp, Ph.D. The goal of this study is to integrate an optimization code with an analysis and design software through its application programming interface (API). Ant colony optimization (ACO) is the optimization procesure being used and the analysis software is SAP2000. The SAP2000 API provides a bridge between ACO code written in Visual Basic 2005 and a steel frame structure modeled in SAP2000. In structural design, the ACO objective function is to minimize the weight of the structure subjected to strength and displacement constraints. The violation of the constraints will be represented by a penalty function which will be enforced on the structural weight. Ordinary moment resisting frames are designed under normal conditions and progressive collapse conditions All the design requirements conform to the American Institute of Steel Construction Load and Resistance Factor Design (AISC-LRFD) Specification. The frame sections are selected from the standard database of AISC W-shapes subjected to some fabrication limitations

Vehicle routing problem considering reconnaissance and transportation

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 문일경.Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This thesis proposes vehicle routing problem considering reconnaissance and transportation (VRPCRT) for troop movements in wartime. VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements. For this thesis, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed and computational experiments were conducted to compare the performance of the ACO algorithm and that of the mixed-integer programming model. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.Abstract iii Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Research Motivation and Contribution 4 1.2 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Review of pickup and delivery problem 6 2.2 Review of ant colony optimization algorithms 9 Chapter 3 Mathematical model 10 3.1 Problem description 10 3.2 The model formulation 14 3.3 Numerical example 17 Chapter 4 Ant colony optimization algorithm 20 4.1 Construction of a solution 21 4.2 Pheromone updating 23 Chapter 5 Computational experiment 26 5.1 Experiment 1 26 5.2 Experiment 2 29 Chapter 6 Conclusion 34 5.1 Findings 34 5.2 Future direction 35 Bibliography 36 국문초록 40 감사의 글 41Maste