SamACO: variable sampling ant colony optimization algorithm for continuous optimization

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants’ solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising

A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach

Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the problem environment.Comment: 4th IEEE International Conference on Information and Automation for Sustainability, 200

Solving SVM model selection problem using ACOR and IACOR

Ant Colony Optimization (ACO) has been used to solve Support Vector Machine (SVM) model selection problem.ACO originally deals with discrete optimization problem. In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretize process would result in loss of some information and hence affect the classification accuracy.In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM. Results have also shown that IACOR-SVM is better than ACOR-SVM in terms of classification accuracy

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Heuristic optimization of RC bridge piers with rectangular hollow sections

This paper deals with the economic optimization of reinforced concrete (RC) bridge piers with hollow rectangular sections and describes the efficiency of three heuristic algorithms: two new variants of the ant colony optimization (ACO) algorithm, the genetic algorithm (GA) and the threshold acceptance (TA) algorithm. The GA and TA are used for comparison with the new ACO algorithms. The total number of variables is 95. All variables are discrete in this analysis. The calibration of the new ACO algorithm recommended a 250-member ant population and 100 stages. The best solution costs 69,467 euros, which means savings of about 33% as compared to experience-based design. Finally, results indicate that the new ACO algorithms are potentially useful for optimizing the costs of real RC structures.This study was funded by the Spanish Ministry of Education (Research Project BIA2006-01444). The authors are grateful for the thorough revision of the manuscript by Dr. Debra Westall.Martínez Martín, FJ.; González Vidosa, F.; Hospitaler Pérez, A.; Yepes Piqueras, V. (2010). Heuristic optimization of RC bridge piers with rectangular hollow sections. Computers and Structures. 88:375-386. https://doi.org/10.1016/j.compstruc.2009.11.009S3753868

Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments

Many real-world optimization problems occur in environments that change dynamically or involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms have been widely applied to dynamic and stochastic problems. This survey gives an overview of major theoretical developments in the area of runtime analysis for these problems. We review recent theoretical studies of evolutionary algorithms and ant colony optimization for problems where the objective functions or the constraints change over time. Furthermore, we consider stochastic problems under various noise models and point out some directions for future research.Comment: This book chapter is to appear in the book "Theory of Randomized Search Heuristics in Discrete Search Spaces", which is edited by Benjamin Doerr and Frank Neumann and is scheduled to be published by Springer in 201

Ant colony optimization routing mechanisms with bandwidth sensing

The study and understanding of the social behavior of insects has contributed to the definition of some algorithms that are capable of solving several types of optimization problems. In 1997 Di Caro and Dorigo developed the first routing algorithm for wired networks, called AntNet, using an approach which was inspired in the behavior of ant colonies. At each node, AntNet, similar to others Ant Colony Optimization (ACO) based algorithms, forward ants based in the amount of pheromones present in the links and in response to the node's queue lengths. In this paper, an adaptation of the e-DANTE algorithm for discrete problems, as an IP based routing mechanism, was implemented. We also propose the inclusion of a new parameter for the computation of paths for both the AntNet and the newly proposed algorithm: the available bandwith. Those methods were tested in ns-2 using two dense network architectures and their efficiency is compared with the original AntNet and a Link-State routing algorithm, when considering the transmission of competing traffic flows between distinct nodes. © 2011 IEEE

Journal of Telecommunications and Information Technology, Load Balancing Based on Optimization Algorithms: An Overview, 2019, nr 4

Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issue

Cognitive Ant Colony Optimization : a new framework in swarm intelligence

Ant Colony Optimization (ACO) algorithms which belong to metaheuristic algorithms and swarm intelligence algorithms have been the focus of much attention in the quest to solve optimization problems. These algorithms are inspired by colonies of ants foraging for food from their nest and have been considered state-of-art methods for solving both discrete and continuous optimization problems. One of the most important phases of ACO algorithms is the construction phase during which an ant builds a partial solution and develops a state transition strategy. There have been a number of studies on the state transition strategy. However, most of the research studies look at how to improve pheromone updates rather than at how the ant itself makes a decision to move from a current position to the next position.The aim of this research is to develop a novel state transition strategy for Ant Colony Optimization algorithms that can improve the overall performance of the algorithms. The research has shown that the state transition strategy in ACO can be improved by introducing non-rational decision-making.The new proposed algorithm is called Cognitive Ant Colony Optimization and uses a new concept of decision-making taken from cognitive behaviour theory. In this proposed algorithm, the ACO has been endowed with non-rational behaviour in order to improve the overall optimization behaviour of ants during the process. This new behaviour will use a non-rational model named prospect theory (Kahneman & Tversky, 1979) to select the transition movements of the ants in the colony in order to improve the overall search capability and the convergence of the algorithm. The new Cognitive Ant Colony Optimization framework has been tested on the Travelling Salesman Problem (TSP), Water Distribution System and Continuous optimization problems. The results obtained show that our algorithm improved the performance of previous ACO techniques considerably