Showing papers on "Ant colony optimization algorithms published in 2009"
21 Apr 2009
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.
Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.
2,424 citations
TL;DR: A novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior, and has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems.
Abstract: Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members search either for ldquofindingrdquo (producer) or for ldquojoiningrdquo (scrounger) opportunities. Based on this framework, concepts from animal searching behavior, e.g., animal scanning mechanisms, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. When tested against benchmark functions, in low and high dimensions, the GSO algorithm has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems. The GSO algorithm is also applied to train artificial neural networks. The promising results on three real-world benchmark problems show the applicability of GSO for problem solving.
658 citations
TL;DR: In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed.
Abstract: Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.
638 citations
TL;DR: This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications, and presents a list of winners and losers.
Abstract: Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.
624 citations
TL;DR: In this paper, a heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses, which is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimizer and harmony search scheme.
452 citations
TL;DR: Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions.
Abstract: This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima.
413 citations
TL;DR: An improved ant colony optimization (IACO) is proposed, which possesses a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve VRP.
412 citations
TL;DR: A Hybrid Big Bang-Big Crunch (HBB-BC) optimization algorithm is employed for optimal design of truss structures and numerical results demonstrate the efficiency and robustness of the H BB-BC method compared to other heuristic algorithms.
387 citations
01 Jun 2009
TL;DR: A novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH), named SACOdm, where d stands for distance and m for memory.
Abstract: In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.
366 citations
01 Jan 2009
TL;DR: This paper proposes an ant colony optimization (ACO) algorithm to schedule large-scale workflows with various QoS parameters and designs seven new heuristics for the ACO approach and proposes an adaptive scheme that allows artificial ants to select heuristic based on pheromone values.
Abstract: Grid computing is increasingly considered as a promising next-generation computational platform that supports wide-area parallel and distributed computing. In grid environments, applications are always regarded as workflows. The problem of scheduling workflows in terms of certain quality of service (QoS) requirements is challenging and it significantly influences the performance of grids. By now, there have been some algorithms for grid workflow scheduling, but most of them can only tackle the problems with a single QoS parameter or with small-scale workflows. In this frame, this paper aims at proposing an ant colony optimization (ACO) algorithm to schedule large-scale workflows with various QoS parameters. This algorithm enables users to specify their QoS preferences as well as define the minimum QoS thresholds for a certain application. The objective of this algorithm is to find a solution that meets all QoS constraints and optimizes the user-preferred QoS parameter. Based on the characteristics of workflow scheduling, we design seven new heuristics for the ACO approach and propose an adaptive scheme that allows artificial ants to select heuristics based on pheromone values. Experiments are done in ten workflow applications with at most 120 tasks, and the results demonstrate the effectiveness of the proposed algorithm.
355 citations
TL;DR: This work presents a novel feature selection algorithm that is based on ant colony optimization that is inspired by observation on real ants in their search for the shortest paths to food sources and shows the superiority of the proposed algorithm on Reuters-21578 dataset.
Abstract: Feature selection and feature extraction are the most important steps in classification systems. Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features which would be impossible to process further. One of the problems in which feature selection is essential is text categorization. A major problem of text categorization is the high dimensionality of the feature space; therefore, feature selection is the most important step in text categorization. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present a novel feature selection algorithm that is based on ant colony optimization. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of genetic algorithm, information gain and CHI on the task of feature selection in Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.
TL;DR: This study proposes a modified harmony search algorithm incorporating particle swarm concept that was applied to the design of four bench-mark networks, with good results.
Abstract: The optimal design of water distribution networks is a non-linear, multi-modal, and constrained problem classified as an NP-hard combinatorial problem. Because of the drawbacks of calculus-based algorithms, the problem has been tackled by assorted stochastic algorithms, such as the genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping algorithm, ant colony optimization algorithm, harmony search, cross entropy, and scatter search. This study proposes a modified harmony search algorithm incorporating particle swarm concept. This algorithm was applied to the design of four bench-mark networks (two-loop, Hanoi, Balerma, and New York City networks), with good results.
01 Jun 2009
TL;DR: A hybrid routing algorithm for MANETs based on ACO and zone routing framework of bordercasting, HOPNET, based on ants hopping from one zone to the next, consists of the local proactive route discovery within a node's neighborhood and reactive communication between the neighborhoods.
Abstract: Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for MANETs. Swarm intelligence is a computational intelligence technique that involves collective behavior of autonomous agents that locally interact with each other in a distributed environment to solve a given problem in the hope of finding a global solution to the problem. In this paper, we propose a hybrid routing algorithm for MANETs based on ACO and zone routing framework of bordercasting. The algorithm, HOPNET, based on ants hopping from one zone to the next, consists of the local proactive route discovery within a node's neighborhood and reactive communication between the neighborhoods. The algorithm has features extracted from ZRP and DSR protocols and is simulated on GlomoSim and is compared to AODV routing protocol. The algorithm is also compared to the well known hybrid routing algorithm, AntHocNet, which is not based on zone routing framework. Results indicate that HOPNET is highly scalable for large networks compared to AntHocNet. The results also indicate that the selection of the zone radius has considerable impact on the delivery packet ratio and HOPNET performs significantly better than AntHocNet for high and low mobility. The algorithm has been compared to random way point model and random drunken model and the results show the efficiency and inefficiency of bordercasting. Finally, HOPNET is compared to ZRP and the strength of nature-inspired algorithm is shown.
TL;DR: The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version, and the harmony search scheme is employed to deal with variable constraints.
TL;DR: The results reveal that simulated annealing and evolution strategies are the most powerful techniques, and harmony search and simple genetic algorithm methods can be characterized by slow convergence rates and unreliable search performance in large-scale problems.
TL;DR: The approach provides an example where an ACO algorithm successfully combines two completely different heuristic measures (with respect to loading and routing) within one pheromone matrix, which clearly outperforms previous heuristics from the literature.
TL;DR: A novel approach (MF/ant algorithm) is proposed to overcome the deficiency of the MF and shows its success using the well known reference ophthalmoscope images of DRIVE database.
TL;DR: Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time of disaster and a multi-objective path selection model is presented to minimize the total travel time along a path and to minimizing the path complexity.
TL;DR: Two novel extensions for the well known ant colony optimization (ACO) framework are introduced here, which allow the solution of mixed integer nonlinear programs (MINLPs) and a hybrid implementation based on this extended ACO framework, specially developed for complex non-convex MINLPs is presented.
TL;DR: A Balanced Ant Colony Optimization (BACO) algorithm for job scheduling in the Grid environment is proposed to balance the entire system load while trying to minimize the makespan of a given set of jobs.
TL;DR: The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study.
Abstract: Ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems through the use of artificial ants and their indirect communication via synthetic pheromones. The first ant algorithms and their development into the Ant Colony Optimisation (ACO) metaheuristic is described herein. An overview of past and present typical applications as well as more specialised and novel applications is given. The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study.
TL;DR: In this article, the authors proposed a fuzzy multiobjective approach for the placement of switches in distribution networks in the presence of distributed generation sources, where the primary objective is reliability improvement with consideration of economic aspects.
Abstract: This paper proposes a methodology for placement of sectionalizing switches in distribution networks in the presence of distributed generation sources. The multiobjective considerations are handled using a fuzzy approach. The primary objective is reliability improvement with consideration of economic aspects. Thus, the objectives are defined as reliability improvement and minimization of the cost of sectionalizing switches. A fuzzy membership function is defined for each term in the objective function according to relevant conditions. Some considerations incorporated in the proposed model are relocation of existing switches and operating constraints on distribution networks and distributed generation (DG) sources during post-fault service restoration. The ant colony optimization (ACO) algorithm is adopted to solve the fuzzy multiobjective problem efficiently. The performance of the proposed approach is assessed and illustrated by various case studies on a test distribution system and also a real distribution network.
TL;DR: A novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability and the results of experiments indicate the superiority of proposed algorithm.
Abstract: Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.
TL;DR: A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes, showing that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.
Abstract: Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.
TL;DR: Two heuristic solution techniques for the multi-objective orienteering problem are developed and applied using the Pareto ant colony optimization algorithm and the design of the variable neighborhood search method, hybridized with path relinking procedures.
Abstract: In this paper, heuristic solution techniques for the multi-objective orienteering problem are developed. The motivation stems from the problem of planning individual tourist routes in a city. Each point of interest in a city provides different benefits for different categories (e.g., culture, shopping). Each tourist has different preferences for the different categories when selecting and visiting the points of interests (e.g., museums, churches). Hence, a multi-objective decision situation arises. To determine all the Pareto optimal solutions, two metaheuristic search techniques are developed and applied. We use the Pareto ant colony optimization algorithm and extend the design of the variable neighborhood search method to the multi-objective case. Both methods are hybridized with path relinking procedures. The performances of the two algorithms are tested on several benchmark instances as well as on real world instances from different Austrian regions and the cities of Vienna and Padua. The computational results show that both implemented methods are well performing algorithms to solve the multi-objective orienteering problem.
TL;DR: This work presents the first runtime analysis of an ACO algorithm, which transfers many rigorous results with respect to the runtime of a simple evolutionary algorithm to the authors' algorithm, and examines the choice of the evaporation factor, a crucial parameter in ACO algorithms, in detail.
Abstract: Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical foundation of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results have been achieved showing that optimal solutions can be obtained in finite time. We present the first runtime analysis of an ACO algorithm, which transfers many rigorous results with respect to the runtime of a simple evolutionary algorithm to our algorithm. Moreover, we examine the choice of the evaporation factor, a crucial parameter in ACO algorithms, in detail. By deriving new lower bounds on the tails of sums of independent Poisson trials, we determine the effect of the evaporation factor almost completely and prove a phase transition from exponential to polynomial runtime.
Journal Article•
TL;DR: First, the basic principles of PSO and improved algorithm were introduced and the research progress on PSO algorithm was summarized in such aspects as organization and evolution of population, hybridPSO algorithm etc.
Abstract: Particle swarm optimization (PSO) with the typical characteristic of swarm intelligence is a kind of novel evolution algorithm after ant colony algorithm. First,the basic principles of PSO and improved algorithm were introduced. Then,the research progress on PSO algorithm was summarized in such aspects as organization and evolution of population, hybrid PSO algorithm etc.
TL;DR: A hybrid metaheuristic for the minimization of makespan in scheduling problems with parallel machines and sequence-dependent setup times by hybridization of an ACO, SA with VNS, combining the advantages of these three individual components.
Abstract: This paper proposes a hybrid metaheuristic for the minimization of makespan in scheduling problems with parallel machines and sequence-dependent setup times. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on an ant colony optimization (ACO), a simulated annealing (SA) for solution evolution, and a variable neighborhood search (VNS) which involves three local search procedures to improve the population. The hybridization of an ACO, SA with VNS, combining the advantages of these three individual components, is the key innovative aspect of the approach. Two algorithms of a hybrid VNS-based algorithm, SA/VNS and ACO/VNS, and the VNS algorithm presented previously are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for large instances.
TL;DR: In this article, a multiobjective ant colony optimization (MACO) has been applied to this problem to minimize the total cost while simultaneously minimizing two distribution network reliability indices including system average interruption frequency index (SAIFI) and system interruption duration index(SAIDI).
TL;DR: A novel hybrid ACO-based classifier model that combines ant colony optimization (ACO) and support vector machines (SVM) to improve classification accuracy with a small and appropriate feature subset is presented.