Showing papers on "Ant colony published in 2012"

Review: A survey: Ant Colony Optimization based recent research and implementation on several engineering domain

Abstract: Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the foraging behaviour of real ant colonies. The ants deposit pheromone on the ground in order to mark the route for identification of their routes from the nest to food that should be followed by other members of the colony. This ACO exploits an optimization mechanism for solving discrete optimization problems in various engineering domain. From the early nineties, when the first Ant Colony Optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. This paper review varies recent research and implementation of ACO, and proposed a modified ACO model which is applied for network routing problem and compared with existing traditional routing algorithms.

Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile)

Abstract: We studied the formation of trail patterns by Argentine ants exploring an empty arena. Using a novel imaging and analysis technique we estimated pheromone concentrations at all spatial positions in the experimental arena and at different times. Then we derived the response function of individual ants to pheromone concentrations by looking at correlations between concentrations and changes in speed or direction of the ants. Ants were found to turn in response to local pheromone concentrations, while their speed was largely unaffected by these concentrations. Ants did not integrate pheromone concentrations over time, with the concentration of pheromone in a 1 cm radius in front of the ant determining the turning angle. The response to pheromone was found to follow a Weber's Law, such that the difference between quantities of pheromone on the two sides of the ant divided by their sum determines the magnitude of the turning angle. This proportional response is in apparent contradiction with the well-established non-linear choice function used in the literature to model the results of binary bridge experiments in ant colonies (Deneubourg et al. 1990). However, agent based simulations implementing the Weber's Law response function led to the formation of trails and reproduced results reported in the literature. We show analytically that a sigmoidal response, analogous to that in the classical Deneubourg model for collective decision making, can be derived from the individual Weber-type response to pheromone concentrations that we have established in our experiments when directional noise around the preferred direction of movement of the ants is assumed.

Cooperative driving: an ant colony system for autonomous intersection management

Abstract: Autonomous intersection management (AIM) is an innovative concept for directing vehicles through the intersections. AIM assumes that the vehicles negotiate the right-of-way. This assumption makes the problem of the intersection management significantly different from the usually studied ones such as the optimization of the cycle time, splits, and offsets. The main difficulty is to define a strategy that improves the traffic efficiency. Indeed, due to the fact that each vehicle is considered individually, AIM faces a combinatorial optimization problem that needs quick and efficient solutions for a real time application. This paper proposes a strategy that evacuates vehicles as soon as possible for each sequence of vehicle arrivals. The dynamic programming (DP) that gives the optimal solution is shown to be greedy. A combinatorial explosion is observed if the number of lanes rises. After evaluating the time complexity of the DP, the paper proposes an ant colony system (ACS) to solve the control problem for large number of vehicles and lanes. The complete investigation shows that the proposed ACS algorithm is robust and efficient. Experimental results obtained by the simulation of different traffic scenarios show that the AIM based on ACS outperforms the traditional traffic lights and other recent traffic control strategies.

Trust based recommender system using ant colony for trust computation

Abstract: Collaborative Filtering (CF) technique has proven to be promising for implementing large scale recommender systems but its success depends mainly on locating similar neighbors. Due to data sparsity of the user-item rating matrix, the process of finding similar neighbors does not often succeed. In addition to this, it also suffers from the new user (cold start) problem as finding possible neighborhood and giving recommendations to user who has not rated any item or rated very few items is difficult. In this paper, our proposed Trust based Ant Recommender System (TARS) produces valuable recommendations by incorporating a notion of dynamic trust between users and selecting a small and best neighborhood based on biological metaphor of ant colonies. Along with the predicted ratings, displaying additional information for explanation of recommendations regarding the strength and level of connectedness in trust graph from where recommendations are generated, items and number of neighbors involved in predicting ratings can help active user make better decisions. Also, new users can highly benefit from pheromone updating strategy known from ant algorithms as positive feedback in the form of aggregated dynamic trust pheromone defines ''popularity'' of a user as recommender over a period of time. The performance of TARS is evaluated using two datasets of different sparsity levels viz. Jester dataset and MovieLens dataset (available online) and compared with traditional Collaborative Filtering based approach for generating recommendations.

The regulation of ant colony foraging activity without spatial information.

Abstract: Many dynamical networks, such as the ones that produce the collective behavior of social insects, operate without any central control, instead arising from local interactions among individuals. A well-studied example is the formation of recruitment trails in ant colonies, but many ant species do not use pheromone trails. We present a model of the regulation of foraging by harvester ant (Pogonomyrmex barbatus) colonies. This species forages for scattered seeds that one ant can retrieve on its own, so there is no need for spatial information such as pheromone trails that lead ants to specific locations. Previous work shows that colony foraging activity, the rate at which ants go out to search individually for seeds, is regulated in response to current food availability throughout the colony's foraging area. Ants use the rate of brief antennal contacts inside the nest between foragers returning with food and outgoing foragers available to leave the nest on the next foraging trip. Here we present a feedback-based algorithm that captures the main features of data from field experiments in which the rate of returning foragers was manipulated. The algorithm draws on our finding that the distribution of intervals between successive ants returning to the nest is a Poisson process. We fitted the parameter that estimates the effect of each returning forager on the rate at which outgoing foragers leave the nest. We found that correlations between observed rates of returning foragers and simulated rates of outgoing foragers, using our model, were similar to those in the data. Our simple stochastic model shows how the regulation of ant colony foraging can operate without spatial information, describing a process at the level of individual ants that predicts the overall foraging activity of the colony.

Ant-Colony-Based Scheduling Algorithm for Energy-Efficient Coverage of WSN

Abstract: Sensors in most wireless sensor networks (WSNs) work with batteries as their energy source, it is usually infeasible to recharge or replace batteries when they discharge. Thus, solving the efficient-energy coverage (EEC) problem is an important issue for a WSN. Therefore, it is necessary to schedule the activities of the devices in a WSN to save the network's limited energy and prolong its lifetime. In this paper, we propose an ant-colony-based acheduling algorithm (ACB-SA) to solve the EEC problem. Our algorithm is a simplified version of the conventional ant colony optimization algorithm, optimized for solving the EEC problem. We also use the probability sensor detection model and apply our proposed algorithm to a heterogeneous sensor set, which represents a more realistic approach to solving the EEC problem. Simulation results are performed to verify the effectiveness of the ACB-SA for solving the EEC problem in comparison with other algorithms.

Ant foraging on complex trails: route learning and the role of trail pheromones in Lasius niger

Abstract: Ants are central place foragers and use multiple information sources to navigate between the nest and feeding sites. Individual ants rapidly learn a route, and often prioritize memory over pheromone trails when tested on a simple trail with a single bifurcation. However, in nature, ants often forage at locations that are reached via more complex routes with multiple trail bifurcations. Such routes may be more difficult to learn, and thus ants would benefit from additional information. We hypothesized that trail pheromones play a more significant role in ant foraging on complex routes, either by assisting in navigation or route learning or both. We studied Lasius niger workers foraging on a doubly bifurcating trail with four end points. Route learning was slower and errors greater on alternating (e.g. left-right) versus repeating routes (e.g. left-left), with error rates of 32 and 3%, respectively. However, errors on alternating routes decreased by 30% when trail pheromone was present. Trail pheromones also aid route learning, leading to reduced errors in subsequent journeys without pheromone. If an experienced forager makes an error when returning to a food source, it reacts by increasing pheromone deposition on the return journey. In addition, high levels of trail pheromone suppress further pheromone deposition. This negative feedback mechanism may act to conserve pheromone or to regulate recruitment. Taken together, these results demonstrate further complexity and sophistication in the foraging system of ant colonies, especially in the role of trail pheromones and their relationship with learning and the use of private information (memory) in a complex environment.

Diversity of Species and Behavior of Hymenopteran Parasitoids of Ants: A Review

Abstract: Reports of hymenopterans associated with ants involve more than 500 species, but only a fraction unambiguously pertain to actual parasitoids. In this paper, we attempt to provide an overview of both the diversity of these parasitoid wasps and the diversity of the types of interactions they have formed with their ant hosts. The reliable list of parasitoid wasps using ants as primary hosts includes at least 138 species, reported between 1852 and 2011, distributed among 9 families from 3 superfamilies. These parasitoids exhibit a wide array of biologies and developmental strategies: ecto- or endoparasitism, solitary or gregarious, and idio- or koinobiosis. All castes of ants and all developmental stages, excepting eggs, are possible targets. Some species parasitize adult worker ants while foraging or performing other activities outside the nest; however, in most cases, parasitoids attack ant larvae either inside or outside their nests. Based on their abundance and success in attacking ants, some parasitoid wasps like diapriids and eucharitids seem excellent potential models to explore how parasitoids impact ant colony demography, population biology, and ant community structure. Despite a significant increase in our knowledge of hymenopteran parasitoids of ants, most of them remain to be discovered.

Hybrid Ant Colony-Genetic Algorithm (GAAPI) for Global Continuous Optimization

Abstract: Many real-life optimization problems often face an increased rank of nonsmoothness (many local minima) which could prevent a search algorithm from moving toward the global solution. Evolution-based algorithms try to deal with this issue. The algorithm proposed in this paper is called GAAPI and is a hybridization between two optimization techniques: a special class of ant colony optimization for continuous domains entitled API and a genetic algorithm (GA). The algorithm adopts the downhill behavior of API (a key characteristic of optimization algorithms) and the good spreading in the solution space of the GA. A probabilistic approach and an empirical comparison study are presented to prove the convergence of the proposed method in solving different classes of complex global continuous optimization problems. Numerical results are reported and compared to the existing results in the literature to validate the feasibility and the effectiveness of the proposed method. The proposed algorithm is shown to be effective and efficient for most of the test functions.

Quality-of-service-enabled ant colony-based multipath routing for mobile ad hoc networks

Abstract: Mobile ad hoc networks (MANETs) are dynamically changing and self-configuring networks. Owing to their widespread use for many applications, multipath routing in MANETs has been widely discussed for providing fault-tolerance routing, quality-of-service (QoS) and various other purposes. The authors propose a quality of service enabled ant colony-based multipath routing (QAMR) algorithm based on the foraging behaviour of ant colony for selecting path and transmitting data. In this approach, the path is selected based on the stability of the nodes and the path preference probability. The authors have considered bandwidth, delay and hop count as the QoS parameters along with the stability of node, number of hops and path preference probability factors. Simulations performed with network simulator 2 shows that the proposed algorithm is scalable and performs better at higher traffic load compared to the existing algorithms.

High dynamic range optimal fuzzy color image enhancement using Artificial Ant Colony System

Abstract: This paper presents a novel approach for the enhancement of high dynamic range color images using fuzzy logic and modified Artificial Ant Colony System techniques. Two thresholds, the lower and the upper are defined to provide an estimate of the underexposed, mixed-exposed and overexposed regions in the image. The red, green and blue (RGB) color space is converted into Hue Saturation and Value (HSV) color space so as to preserve the chromatic information. Gaussian MFs suitable for the underexposed and overexposed regions of the image are used for the fuzzification. Parametric sigmoid functions are used for enhancing the luminance components of under and over-exposed regions. Mixed-exposed regions are left untouched throughout the process. An objective function comprising of Shannon entropy function as the information factor and visual appeal indicator is optimized using Artificial Ant Colony System to ascertain the parameters needed for the enhancement of a particular image. Visual appeal is preferred over the consideration of entropy so as to make the image human-eye-friendly. Separate power law operators are used for the saturation adjustment so as to restore the lost information. On comparison, this approach is found to be better than the bacterial foraging (BF)-based approach [1].

Efficient Transit Schedule Design of timing points: A comparison of Ant Colony and Genetic Algorithms

Abstract: This work defines Transit Schedule Design (TSD) as an optimization problem to construct the transit schedule with the decision variables of the location of timing points and the amount of slack time associated with each timing point. Two heuristic procedures, Ant Colony and Genetic Algorithms, are developed for constructing optimal schedules for a fixed bus route. The paper presents a comparison of the fundamental features of the two algorithms. They are then calibrated based on data generated from micro-simulation of a bus route in Melbourne, Australia, to give rise to (near) optimal schedule designs. The algorithms are compared in terms of their accuracy and efficiency in providing the minimum cost solution. Although both procedures prove the ability to find the optimal solution, the Ant Colony procedure demonstrates a higher efficiency by evaluating less schedule designs to arrive at a 'good' solution. Potential benefits of the developed algorithms in bus route planning are also discussed.

A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems

Abstract: Ant Colony Optimization (ACO) is a popular optimization paradigm inspired by the capabilities of natural ant colonies of finding shortest paths between their nest and a food source. This has led to many successful applications for various combinatorial problems. The reason for the success of ACO, however, is not well understood and there is a need for a rigorous theoretical foundation. We analyze the running time of a simple ant colony optimizer for stochastic shortest path problems where edge weights are subject to noise that reflects delays and uncertainty. In particular, we consider various noise models, ranging from general, arbitrary noise with possible dependencies to more specific models such as independent gamma-distributed noise. The question is whether the ants can find or approximate shortest paths in the presence of noise. We characterize instances where the ants can discover the real shortest paths efficiently. For general instances we prove upper bounds for the time until the algorithm finds reasonable approximations. Contrariwise, for independent gamma-distributed noise we present a graph where the ant system needs exponential time to find a good approximation. The behavior of the ant system changes dramatically when the noise is perfectly correlated as then the ants find shortest paths efficiently. Our results shed light on trade-offs between the noise strength, approximation guarantees, and expected running times.

Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm

Abstract: This study introduces the ant colony optimisation (ACO) algorithm implemented in the hyper-cube (HC) framework to solve the distribution network minimum loss reconfiguration problem. The ACO is a relatively new and powerful intelligence evolution method for solving optimisation problems. It is a population-based approach inspired from natural behaviour of real ant colonies. In contrast to the usual ways of implementing ACO algorithms, the HC framework limits the pheromone values by introducing changes in the pheromone updating rules resulting in a more robust and easier to implement version of the ACO procedure. The optimisation problem is formulated taking into account the operational constraints of the distribution systems. Results of numerical tests carried out on three test systems from literature are presented to show the effectiveness of the proposed approach.

Ants easily solve stochastic shortest path problems

Abstract: The first rigorous theoretical analysis (Horoba, Sudholt (GECCO 2010)) of an ant colony optimizer for the stochastic shortest path problem suggests that ant system experience significant difficulties when the input data is prone to noise. In this work, we propose a slightly different ant optimizer to deal with noise.We prove that under mild conditions, it finds the paths with shortest expected length efficiently, despite the fact that we do not have convergence in the classic sense. To prove our results, we introduce a stronger drift theorem that can also deal with the situation that the progress is faster when one is closer to the goal.

The urban bus routing problem in the Tunisian case by the hybrid artificial ant colony algorithm

Abstract: The problem statement tackled in this paper is concentrated on the school bus routing problem (SBRP) in urban areas. This problem is a variant of the vehicle routing problem where we identify three simultaneous decisions that have to be made: determining the set of stops to visit, for each student which stop he should walk to and the latter case occurs when determining the routes visited with the chosen stops, so that the total traveled distance is minimized. Accordingly, to the Tunisian case study and the difficulty to solve it in a manual manner we resort to metaheuristic approaches. We have developed a hybrid evolutionary computation based on an artificial ant colony with a variable neighborhood local search algorithm. Empirically we demonstrate that our algorithm yields consistently better results.

A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems

Abstract: The vehicle routing problemVRPis a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligenceSIapproaches, ant colony optimizationACOand particle swarm optimization � PSO� , for solving capacitated vehicle routing problemsCVRPs� . In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.

Supercolonies of billions in an invasive ant: What is a society?

Abstract: All societies are characterized by the capacity of their members to distinguish one another from outsiders. Ants are among the species that form ‘‘anonymous societies’’: members are not required to tell each other apart as individuals for the group to remain unified. Rather, each society depends on shared cues recognized by all its members. These cues permit societies to reach populations in the low millions in certain ant and termite species, and to grow indefinitely populous, expansive, and possibly long lasting in a few other ant species, which are described as having supercolonies. Anonymous societies are contrasted with ‘‘individual recognition societies’’ such as those of most vertebrates, which are limited to a few individuals by the necessity that the members individually recognize each other. The shared recognition cues of ants provide clear criteria for defining colonies and are what enables a supercolony to remain a single society no matter how large it becomes. I examine the often conflicting ideas about the best studied ant with supercolonies, the Argentine ant (Linepithema humile). Its invasive supercolonies, containing in some cases billions of workers and queens spread over hundreds of square kilometers, can be most parsimoniously understood as single colonies that have had an opportunity to expand across regions of suitable habitat because of a lack of well-matched competitors. This capacity for unrestricted growth is the defining characteristic of supercolonies. There is no evidence that the local patchiness of nests and patterns of worker and food traffic within these wide-ranging populations are so invariant that supercolonies do not exist but instead are collections of numerous independent nest clusters that should be called ‘‘colonies.’’ Nor is there evidence for the hypothesis that invasive supercolonies have been able to grow large and successful overseas only as a result of evolving through genetic drift or selection to become fundamentally different from the smaller colonies typical of the species’ region of origin around northern Argentina. The most unique feature of the Argentine ant, however, is not that its colonies are anonymous or that they can grow indefinitely large—though the last trait is found only in a few ant species and humans. Rather, it is that Argentine ant colonies do not interbreed. Indeed, the only fighting among Argentine ants occurs along colony borders, which even reproductives seldom, if ever, cross and survive. For this reason, each Argentine ant supercolony acts as virtually a sibling species. Key words: Argentine ant, polydomy, recognition system, reproductive isolation, society, speciation, supercolony, unicolonial. [Behav Ecol]

An ant colony system based decision support system for construction time-cost optimization

Abstract: Time and cost are the two most important factors to be considered in every construction project. In order to maximize the profit, both the client and contractor would strive to minimize the project duration and cost concurrently. In the past, most of the research studies related to construction time and cost assumed time to be constant, leaving the analyses based purely on a single objective of cost. Acknowledging this limitation, an evolutionary-based optimization algorithm known as an ant colony system is applied in this study to solve the multi-objective time-cost optimization problems. In this paper, a model is developed using Visual Basic for Application™ which is integrated with Microsoft Project™. Through a test study, the performance of the proposed model is compared against other analytical methods previously used for time-cost modeling. The results show that the model based on the ant colony system techniques can generate better solutions without utilizing excessive computational resour...

Ant colony system based PI maximum power point tracking for stand alone photovoltaic system

Abstract: This paper introduces a social inspired optimization based control method for the maximum power point tracking (MPPT) of a photovoltaic (PV) system. In particular, a well known proportional plus Integral (PI) controller is fine tuned using ant colony system algorithm and tested on a simulated stand alone PV array with battery load. The proposed ant colony based PI-MPPT controller is enhanced by the fractional open circuit voltage technique to rapidly and accurately track the maximum power point (MPP). The design algorithm of this controller is presented together with its simulation results. Satisfactory dynamic and steady state performance of the simulated PV system even under variable irradiance levels reflects the superiority of the proposed control technique over the traditional control methods.

Controlled Mobility Sensor Networks for Target Tracking Using Ant Colony Optimization

Abstract: In mobile sensor networks, it is important to manage the mobility of the nodes in order to improve the performances of the network. This paper addresses the problem of single target tracking in controlled mobility sensor networks. The proposed method consists of estimating the current position of a single target. Estimated positions are then used to predict the following location of the target. Once an area of interest is defined, the proposed approach consists of moving the mobile nodes in order to cover it in an optimal way. It thus defines a strategy for choosing the set of new sensors locations. Each node is then assigned one position within the set in the way to minimize the total traveled distance by the nodes. While the estimation and the prediction phases are performed using the interval theory, relocating nodes employs the ant colony optimization algorithm. Simulations results corroborate the efficiency of the proposed method compared to the target tracking methods considered for networks with static nodes.

The Direct and Ecological Costs of an Ant-Plant Symbiosis

Abstract: How strong is selection for cheating in mutualisms? The answer depends on the type and magnitude of the costs of the mutualism. Here we investigated the direct and ecological costs of plant defense by ants in the association between Cordia nodosa, a myrmecophytic plant, and Allomerus octoarticulatus, a phytoecious ant. Cordia nodosa trees produce food and housing to reward ants that protect them against herbivores. For nearly 1 year, we manipulated the presence of A. octoarticulatus ants and most insect herbivores on C. nodosa in a full-factorial experiment. Ants increased plant growth when herbivores were present but decreased plant growth when herbivores were absent, indicating that hosting ants can be costly to plants. However, we did not detect a cost to ant colonies of defending host plants against herbivores. Although this asymmetry in costs suggests that the plants may be under stronger selection than the ants to cheat by withholding investment in their partner, the costs to C. nodosa are p...

Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm

Abstract: In this paper, a two-stage ant colony optimisation (ACO) algorithm is implemented in a multi-agent system (MAS) to accomplish integrated process planning and scheduling (IPPS) in the job shop type flexible manufacturing environments. Traditionally, process planning and scheduling functions are performed sequentially and the actual status of the production facilities is not considered in either process planning or scheduling. IPPS is to combine both the process planning and scheduling problems in the consideration, that is, the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. The ACO algorithm can be applied to solve IPPS problems. An innovative two-stage ACO algorithm is introduced in this paper. In the first stage of the algorithm, instead of depositing pheromones on graph edges as in common ant algorithms, ants are directed to deposit pheromones at the nodes to select a set of more favourable processes. In th...

Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems

Abstract: This paper proposes a framework named multi-objective ant colony optimization based on decomposition (MoACO/D) to solve bi-objective traveling salesman problems (bTSPs). In the framework, a bTSP is first decomposed into a number of scalar optimization subproblems using Tchebycheff approach. To suit for decomposition, an ant colony is divided into many subcolonies in an overlapped manner, each of which is for one subproblem. Then each subcolony independently optimizes its corresponding subproblem using single-objective ant colony optimization algorithm and all subcolonies simultaneously work. During the iteration, each subproblem maintains an aggregated pheromone trail and an aggregated heuristic matrix. Each subcolony uses the information to solve its corresponding subproblem. After an iteration, a pheromone trail share procedure is evoked to realize the information share of those subproblems solved by common ants. Three MoACO algorithms designed by, respectively, combining MoACO/D with AS, MMAS and ACS are presented. Extensive experiments conducted on ten bTSPs with various complexities manifest that MoACO/D is both efficient and effective for solving bTSPs and the ACS version of MoACO/D outperforms three well-known MoACO algorithms on large bTSPs according to several performance measures and median attainment surfaces.