Behavior Of Ant Colonies Research Articles

An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy images

An accurate cell tracking approach with self-regulated foraging behavior of ant colonies in dynamic microscopy images

Penentuan Jalur Terpendek dengan Metode Heuristik Menggunakan Algoritma Sarang Semut (Ant Colony)

Ant Colony algorithm was adopted from the behavior of ant colonies, known as the system of ants, ant colonies are naturally able to find the shortest route on their way from nest to food source places. Colony of ants can find shortest route between the nest and food sources based on the trajectory of footprints that have been passed. The density of ant footprints on the path is always updating because of the evaporation of the footprints and the determination of ant pathways using probability calculations. This study aims to determine the results of determining the shortest path using the ant colony algorithm as the best route from the Samarinda City secondary arterial road with the route starts from Slamet Riyadi road to DI Panjaitan road. Based on the results of the study using the ant colony algorithm obtained the shortest path of 8.307 kilometers with footprint density of 1.005.

Energy efficient multipath ant colony based routing algorithm for mobile ad hoc networks

This paper describes the novel wireless routing protocol made for mobile ad hoc networks or wireless sensor networks using the bio-inspired technique. Bio-inspired algorithms include the routing capabilities taken from the social behavior of ant colonies, bird flocking, honey bee dancing, etc and promises to be capable of catering to the challenges posed by wireless sensors. Some of the challenges of wireless sensor networks are limited bandwidth, limited battery life, low memory, etc. An energy-efficient multipath routing algorithm based on the foraging nature of ants is proposed including many meta-heuristic impact factors to provide good robust paths from source to destination to overcome the challenges faced by resource-constrained sensors. Analysis of individual impact factor is represented which justifies their importance in routing performance. The multi-path routing feature is claimed by showing energy analysis as well as statistical analysis in-depth to the readers. The proposed routing algorithm is analyzed by considering various performance metrics such as throughput, delay, packet loss, network lifetime, etc. Finally, the comparison is done against AODV routing protocol by considering performance metrics where the proposed routing algorithm shows a 49% improvement in network lifetime.

Movement, Encounter Rate, and Collective Behavior in Ant Colonies.

Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.

AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is a mobile and wireless network that consists of connected vehicles, and stationary nodes called roadside units (RSUs) placed on the aboard of roads to improve traffic safety and to ensure drivers’ and passengers’ comfort. However, deploying RSUs is one of the most important challenges in VANETs due to the involved placement, configuration, and maintenance costs in addition to the network connectivity. This study focuses on the issue of deploying a set of RSUs that is able to maximize network coverage with a reduced cost. In this paper, we propose a new formulation of RSUs deployment issue as a maximum intersection coverage problem through a graph-based modeling. Moreover, we propose a new bio-inspired RSU placement system called Ant colony optimization system for RSU deployment in VANET (AC-RDV). AC-RDV is based on the idea of placing RSUs within the more popular road intersections, which are close to popular places like touristic and commercial areas. Since RSU deployment problem is considered as NP-Hard, AC-RDV inspires by the foraging behavior of real ant colonies to discover the minimum number of RSU intersections that ensures the maximum network connectivity. After a set of simulations and comparisons against traditional RSU placement strategies, the results obtained showed the effectiveness of the proposed AC-RDV in terms of number of RSUs placed, the average area coverage, the average connectivity and the overlapping ratio.

Modified swarm algorithm «ant tree» in the problem of diversification of tractor resources spaces

In this work, the problem of increasing traceability is solved by diversifying trace resources, which consists in using additional areas adjacent to the channels below and above, with a trace layer located above the “over-the-cell” cells. For a single-layer routing uses a new paradigm of combinatorial optimization – ant tree (trees ant colony optimization (T-ACO)), based on the ideas of adaptive behavior of ant colony. The decision tree is used as a decision search graph. An agent for the decision search graph does not create a route, but a tree, which in its structure coincides with the representation of the solution to the trace problem in the «over-the-cell» region. This eliminates the use of additional transformations in the decoding process of decisions that allow interpretation of decisions in the form of trees and allows you to discard a lot of «illegal» solutions, which leads to an increase in the quality of the solutions obtained. Using the tracing procedure in «over-the-cell» based on the ant algorithm allows you to unload the channel by 15-20%. The time complexity of the algorithm depends on the number of vertices n of the decision search graph, the number of agents y, the number of iterations l, and is defined as O (n2·l·y).

The effect of nest topology on spatial organization and recruitment in the red ant Myrmica rubra.

Nests of social insects are an important area for the exchange of food and information among workers. We investigated how the topology of nest chambers (as opposed to nest size or environmental factors) affects the spatial distribution of nestmates and the foraging behavior of Myrmica rubra ant colonies. Colonies were housed in artificial nests, each with same-sized chambers differing in the spatial arrangement of galleries. A highly connected central chamber favored higher occupancy rates and a more homogeneous distribution of ants across chambers. In contrast, a chain of successive chambers led to a more heterogeneous distribution of ants, with the occupancy of a chamber chiefly mediated by its distance to the entrance. Irrespective of nest topology, the entrance chamber housed the largest proportion of ants, often including the queen, which exhibited a preference for staying in densely populated chambers. Finally, we investigated how nest topology influenced nestmate recruitment. Surprisingly, a highly connected chamber in the center of the nest did not promote greater recruitment nor activation of ants. At the onset of foraging, the largest number of moving ants was reached in the topology where the most connected chamber was the nest entrance. Later in the process, we found that a chain of successive chambers was the best topology for promoting ant's mobilization. Our work demonstrates that nest topology can shape the spatial organization and the collective response of ant colonies, thereby taking part in their adaptative strategies to exploit environmental resources.

Ant Miner

Discovering classification rules from large data is an important task of data mining and is gaining considerable attention. This article presents a novel ant miner for classification rule mining. Our ant miner is inspired by research on the behavior of real ant colonies, simulated annealing, and some data mining concepts as well as principles. Here we present a Michigan style approach for single objective classification rule mining. The algorithm is tested on a few benchmark datasets drawn from UCI repository. Our experimental outcomes confirm that ant miner-HMC (Hybrid Michigan Style Classification) is significantly better than ant-miner-MC (Michigan Style Classification).

Colony size, nestmate density and social history shape behavioural variation in Formica fusca colonies

AbstractIn ants, individuals live in tightly integrated units (colonies) and work collectively for its success. In such groups, stable intraspecific variation in behaviour within or across contexts (personality) can occur at two levels: individuals and colonies. This paper examines how colony size and nestmate density influence the collective exploratory behaviour of Formica fusca (Hymenoptera: Formicidae), in the laboratory. The housing conditions of the colonies were manipulated to vary the size of colonies and their densities under a fully factorial design. The results demonstrate the presence of colony behavioural repeatability in this species, and contrary to our expectations, colonies were more explorative on average when they were kept at lower nestmate densities. We also found that experimental colonies created from larger source colonies were more explorative, which conveys that a thorough understanding of the contemporary behaviour of a colony may require knowing its social history and how it was formed. Our results also convey that the colony size and nestmate density can have significant effects on the exploratory behaviour of ant colonies.

Implementacija algoritma kolonije mrava za optimalnu rekonfiguraciju distributivne mreže

The paper analyzes the possibility of application of Ant Colony Optimization (ACO) algorithm for reconfiguration of distribution network with the aim of active power minimiza- tion. ACO is a population-based meta-heuristic technique used to solve different combinatorial optimization problems. The search technique is inspired by the behaviour of ant colonies in nature. The efficiency of the proposed algorithm is demonstrated on IEEE 33-bus and IEEE 69-bus test distribution systems. Also, the results obtained by using ACO algorithm are compared to the results achievable by other heuristic and meta-heuristic algorithms.

Ant Miner

In data mining the task of extracting classification rules from large data is an important task and is gaining considerable attention. This article presents a novel ant miner for classification rule mining. The ant miner is inspired by researches on the behaviour of real ant colonies, simulated annealing, and some data mining concepts as well as principles. This paper presents a Pittsburgh style approach for single objective classification rule mining. The algorithm is tested on a few benchmark datasets drawn from UCI repository. The experimental outcomes confirm that ant miner-HPB (Hybrid Pittsburgh Style Classification) is significantly better than ant-miner-PB (Pittsburgh Style Classification).

Pencarian Jalur Terpendek Menggunakan Metode Algoritma “Ant Colony Optimization” Pada GUI Matlab (Studi Kasus: PT Distriversa Buana Mas cabang Purwokerto)

For company, shortest distribution route is an important thing to be developed in order to obtain effectiveness in the distribution of products to consumers. One way of development is to find the shortest route with Ant Colony Optimization algorithm. This algorithm is inspired by the behavior of ant colonies that can find the shortest path from the nest to the food source. One example of a distribution company is PT Distriversa Buana Mas, also known as DBM. DBM is a physical distribution company covering the entire Indonesian archipelago specialized in the distribution of pharmaceuticals and consumer goods such as personal care, cosmetic and food products. DBM uses land transportation in 18 brances spread across Indonesia. One branch of DBM is in the Purwokerto region that distributes products to 29 stores in the Purbalingga region. This research is done with the help of GUI as a computation tool. Based on test results, the GUI system that has been built able to simplify and speed up the selection process of finding the shortest route for distribute product of DBM in the Purbalingga region. Keywords: Travelling Salesman Problem, Distriversa Buana Mas, Algorithm, Ant Colony Optimization, GUI

Sanitary behavior in queenright and queenless ant colonies

Waste disposal is important for maintaining the health of animal societies. Adults and off ;spring produce large amounts of waste and feces that could contain pathogens or toxins and may need to be stored away from the young or adult individuals. In social insects, the worker caste is responsible for nest maintenance, including sanitary behavior, and waste disposal strategies vary between species. However, individual task allocation is generally affected by queen presence and worker efficiency often decreases in the absence of a queen.Here we show that most (74%) colonies of the cavity-dwelling ant Temnothorax crassispinus construct up to two localized indoor ‘latrines’, which are used for defecation and only very rarely also as waste dumps. Restriction of defecation to designated areas affects the growth of mold inside the nest. Defecation strategies of colonies are furthermore affected by queen presence, with workers from queenless nests more frequently defecating outside the nest and forming latrines. As colonies do not actively avoid moldy nests, mold seems to not necessarily be a threat to the colony. While solid waste management has been more extensively studied in social insects, this study contributes a rare insight into the organization of non-easily transportable fecal waste.

Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for optimizing PID parameters on Autonomous Underwater Vehicle (AUV) control system

Indonesia consists of seventy percents of sea such that Indonesia has much marine resource. For exploring marine resource, it is required Autonomous Underwater Vehicle (AUV) with its control. In AUV, there are surge, sway, heave position and roll, pitch, yaw angle which have to be controlled. PID (Proportional-Integral-Derivative) control has been developed in many control system problems. In previous research, the tuning of PID parameters such as Kp, Ki, and Kd has been applied by Ziegler-Nichols technique. In this research, the optimization of PID parameters will be approached by heuristic methods such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). PSO is inspired by the flock of birds or fishes in food search while ACO is inspired by the cooperative behavior of ant colonies, to find the shortest path from their nest to the food source. Either particle in PSO or path consisting pheromone in ACO represents PID parameters and the fitness function is integral of absolute error (IAE). Based on simulations, heuristic methods can result responses with small overshoot and fast rise time and settling time.

Fragmentation in Distributed Database Design Based on Ant Colony Optimization Technique

Distributed database design solutions depend heavily on the exploitation of input data sources by using clustering techniques in data mining. A new approach of biomimetic computation systems such as ant colony optimization (ACO) for this solution is of interest to informatics experts. Using ACO techniques for this solution has the advantages such as faster algorithms thanks to the randomness of ant colony behavior. The use of random numbers based on heuristic information to pickup (drop) points will facilitate the flexible search on a large data space, so that it provides us with a better answer. In this article, the authors present ACO algorithms application solutions to clustering techniques for the problem of vertical fragmentation of distributed data.

A Framework of Ant Colony P System

Ant colony algorithm is a swarm intelligence population-based algorithm, which is inspired from the foraging behavior of ant colonies. Nowadays, the ant colony algorithm has become a very popular topic, and has been widely used in many fields. Also, in the field of P systems, there are many researchers using ant colony algorithms as subalgorithms in their researches, while ant colony algorithms are not implemented in the form of evolutionary rules. In this paper, we proposed an ant colony P system $\Pi _{ACPS}$ based on the pure evolutionary rule. $\Pi _{ACPS}$ not only can provide a basic computational framework for the needy P systems researchers, but also can maximally perform the parallelism of ant colony algorithms. An example of solving TSP given in the article illustrates the effectiveness and feasibility of $\Pi _{ACPS}$ .

Packets routing and bandwidth sensing in a network traffic: Ant colony optimization tactic.

Packets routing and bandwidth sensing in a network platform remains an integral part of the study of signal flow.The algorithm to route packets in a network link called the AntNet algorithm was inspired by the behavior of real ant colonies. At each node in the network, a forward ant deposits some amount of pheromones at different links that responds to the node’s queue length. In this paper, we propose the inclusion of the computation of paths to adapt with the Depth Search Ant Explorer Network (DS-ANTENet) algorithm for discrete problems as an IP based mechanism. This method is tested and the efficiency is compared to the original AntNet algorithm and the Link-State algorithm to check the transmission of computing traffic flows between the nodes. We then made comparison with the algorithms proposed in the literature. The protocols were sorted out in terms of average number of lost packets ranging from the higher priority queue to the lower priority queue which then resulted to the fact that; First, AntNetBW (loss ratios reduction of 9.6% when compared to the AntNet and the Link-State algorithm respectively. Secondly, SANTENetBW (loss ratios reduction of 8.3% and 36.7% when compared to the AntNet and the Link-State algorithm respectively. Finally, DS-ANTENet (loss ratios reduction 0.7% and 33.2% when compared to the AntNet and the Link- State algorithm respectively.Keywords: Packets Routing, Bandwidth Sensing, Network Traffic, Ant Colony Optimization Algorithm, AntNet

Penerapan Algoritma Semut Dalam Penentuan Distribusi Jalur Pipa Pengolahan Air Bersih

In general, the shortest path search can be divided into two methods, namely conventional methods and heuristic methods. Conventional methods tend to be more easily understood than heuristic methods, but when compared to the results obtained, heuristic methods are more varied and the time required for calculation is shorter. In the heuristic method there are several algorithms, one of which is the ant algorithm. An ant algorithm is an algorithm that is adopted from the behavior of ant colonies. Naturally ant colonies are able to find the shortest route on the way from the nest to the food sources. Ant colonies can find the shortest route between the nest and the source of food based on footprints on the trajectory that has been passed. The more ants that pass through a path, the more obvious the footprints will be. Ants Algorithms are very appropriate to be applied in solving optimization problems, one of which is to determine the shortest path. This study aims to facilitate the Development of Drinking Water Treatment Performance to make decisions in determining the point where the installation of water distribution pipelines that will be distributed to residents' homes. This study took 8 points of clean water treatment pipeline with starting point A and point N. Based on the calculation of clean water pipeline between A and N by using ant algortima, from two cycles passed, it is proven that the shortest path is only one pipeline, pipe N as destination with route length 4 as V1 → V2 → V3 → V4 → V8.

Penerapan Algoritma Semut Dalam Penentuan Distribusi Jalur Pipa Pengolahan Air Bersih

In general, the shortest path search can be divided into two methods, namely conventional methods and heuristic methods. Conventional methods tend to be more easily understood than heuristic methods, but when compared to the results obtained, heuristic methods are more varied and the time required for calculation is shorter. In the heuristic method there are several algorithms, one of which is the ant algorithm. An ant algorithm is an algorithm that is adopted from the behavior of ant colonies. Naturally ant colonies are able to find the shortest route on the way from the nest to the food sources. Ant colonies can find the shortest route between the nest and the source of food based on footprints on the trajectory that has been passed. The more ants that pass through a path, the more obvious the footprints will be. Ants Algorithms are very appropriate to be applied in solving optimization problems, one of which is to determine the shortest path. This study aims to facilitate the Development of Drinking Water Treatment Performance to make decisions in determining the point where the installation of water distribution pipelines that will be distributed to residents' homes. This study took 8 points of clean water treatment pipeline with starting point A and point N. Based on the calculation of clean water pipeline between A and N by using ant algortima, from two cycles passed, it is proven that the shortest path is only one pipeline, pipe N as destination with route length 4 as V1 → V2 → V3 → V4 → V8.

Foraging behavior and locomotion of the invasive Argentine ant from winter aggregations.

The collective behavior of ant colonies, and locomotion of individuals within a colony, both respond to changing conditions. The invasive Argentine ant (Linepithema humile) thrives in Mediterranean climates with hot, dry summers and colder, wet winters. However, its foraging behavior and locomotion has rarely been studied in the winter. We examined how the foraging behavior of three distinct L. humile colonies was related to environmental conditions and the locomotion of workers during winter in northern California. We found that colonies foraged most between 10 and 15°C, regardless of the maximum daily temperature. Worker walking speed was positively associated with temperature (range 6–24°C) and negatively associated with humidity (range 25–93%RH). All colonies foraged during all day and night hours in a predictable daily cycle, with a correlation between the rate of incoming and outgoing foragers. Foraging activity was unrelated to the activity of a competing native ant species, Prenolepis imparis, which was present in low abundance, and ceased only during heavy rain when ants left foraging trails and aggregated in small sheltered areas on trees.