Behavior Of Ant Colonies Research Articles

Optimization of the Colony Size Parameter in the Ant Algorithm for Solving the Routing Problem in Communication Networks

With the increasing amount of information and complexity of communication networks, the issue of routing remains relevant. The routing problem is a classic problem of building a logical structure of a communication network. To solve this problem, many methods are used, such as: genetic algorithms, ant algorithm, swarm intelligence, artificial bee colony algorithm, firefly algorithm, bacterial food search optimization algorithm, etc. Despite the fact that the effectiveness of using the ant algorithm is widely known and described, however, there is no single approach to finding the optimal use case for the algorithm. There is a need to determine the optimal size of the colony when solving the routing problem. The article describes the results of the simulation process of modeling the behavior of ant colonies of different sizes to find the optimal route through the AnyLogic environment. It is determined that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks. To find the optimal ratio of the colony size to the complexity of the task, you can use the grid search methodology. The results clearly demonstrate that with increasing complexity of the task (the number of communication nodes), the same number of ants cope worse with the task. The problem associated with the need to accurately determine the optimal size of a colony is of great practical importance, because an increase in the size of a colony has generally decreasing utility. It was found that for many, an increase in the number of ants from 100 to 500 had a relatively greater effect than an increase in the size of the colony from 1000 to 10000. It is proved that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks.

Unraveling the forces shaping foraging dynamics in harvester ant colonies: Recruitment efficiency and environmental variability

The collective foraging behavior of ant colonies is a central focus in behavioral ecology. This paper enhances the classical model of foraging dynamics in harvester ant colonies by introducing a nonlinear recruitment rate and considering environmental variability. Initially, we analyze the existence and stability of steady states in the deterministic model. The results suggest that an increase in mean recruitment time can reduce the foraging threshold, leading to both forward and backward bifurcations. Furthermore, both average recruitment time and the interference intensity of recruiters impact the number of workers in each subgroup. Subsequently, we conduct an analysis of the long-term and transient dynamics of collective foraging in random environments, providing sufficient conditions for the colony to sustain foraging activity. The findings emphasize the scene-dependent impact of environmental stochasticity on foraging dynamics. When ant colonies deterministically cease foraging, environmental stochasticity may unexpectedly prolong the foraging state. Conversely, when colonies deterministically persist in foraging, environmental stochasticity may disrupt this continuity. Additionally, the effect of environmental stochasticity on foraging status varies with the initial worker size. Sizes near the boundary of the basin of attraction between non-foraging and foraging states exhibit greater sensitivity to environmental stochasticity, and sufficiently large stochasticity can impact foraging dynamics across a broader range of initial worker sizes. These findings underscore the intricate interplay between intrinsic factors (e.g., recruitment efficiency and interference intensity) and extrinsic factors (e.g., environmental stochasticity) in shaping the collective foraging dynamics of ant colonies.

Analyze the Impact of Mobility and Pause Time on ACO based Manet Routing Protocols - Review

Abstract: Recently, many researchers have studied its performance within Mobile Ad-hoc Networks (MANETs). Every ad hoc network protocol focuses on a different set of measurements and traits. The majority of the currently utilized mobility models are employed to provide realistic movement patterns for MANET scenarios. A crucial element in the creation of MANET protocols is simulation. The future topology of a network can be predicted, which enables Quality of service- aware routing to choose a dependable link for data transmission. Ant algorithms, also known as swarm intelligence, are a wider field of study that deals with algorithmic approaches that are motivated by the behaviour of ant colonies and other insects. Ant Colony Optimization (ACO) is a subset of this larger topic. Based on the pheromone value deposited over the link, we identified two categories for the link quality in the proposed approach. We have demonstrated how the network's performance is enhanced by the suggested technique. The nodes are moving in the specified terrain dimensions through the Enhanced Manhattan Mobility Model (EMMM). The five QoS performance metrics are analyzed over three MANET routing protocols. The comparison is made over two important factors-mobility and pause time with the proposed ACO technique of reliable links. In the first experiment of different speeds, DSR is 25.5% better than AODV and 5.3% better than DSDVin metric packet dropping, 1.15% better than AODV and 1.01% better than DSDV in Packet Delivery Ratio, 1.5% better than AODV and 1.04% better than DSDV in packet overhead, and 62.7% better than AODV and 73.1% better than DSDV in average end-to-end delay. In the second experiment of pause times, DSR is 25.05% better than AODV and 4.5% better than DSDV in packet dropping, 1.18% better than AODV and 1.01% better than DSDV in packet delivery ratio, 1.01% better than DSDV in throughput, 0 to 0.002% in packet overhead, and 0.02% to 0.005% shorter delay.

Parameters Sensitivity Analysis of Ant Colony Based Clustering: Application for Student Grouping in Collaborative Learning Environment

Clustering analysis is one of the data analysis techniques that organizes items into clusters according to their degrees of similarities. In this context, bio-inspired algorithms have found success in solving clustering problems.Inspired by nature, Ant Colony based Clustering arises from ant colony behavior in organizing nests and clustering ants corpses. Accordingly, several researchers proposed different clustering algorithms that mimic the real ants behavior in forming cemeteries. However, the performance of a given algorithm depends strongly on its parameters settings. Indeed, it holds a large number of adjustable parameters that need to be instantiated by suitable values. In this paper, we study the parameters influence, more precisely the parameter α which is responsible for adjusting similarity between objects. In fact, we analyze the impact of α values on the performance of some well known Ant Colony based Clustering Algorithms applied to constructing team-works in a collaborative learning environment. After various bench tests, the choice of α value is determined based on the best algorithm accuracy for each learning data-set. The experimental results prove that Ant Colony algorithms performance strongly depends on α, especially when applied to high-dimensional data-sets. However, α has a negligible influence on the algorithm’s accuracy when applied to low-dimensional data-sets. Obviously, the feature selection step could be ignored since it has a negligible influence on the algorithm performance even with different values of α.

Intelligent Optimization of CMOS Operational Amplifier Using 3D Ant Colony Optimization

This paper presents the use of an artificial intelligence (AI) tool based on ant colony behavior to design an operational amplifier circuit. The ant colony optimization (ACO) is implemented in a hybrid evolutionary sizing method to automate analog circuit design. This new meta-heuristic approach that combines the artificial intelligence of an ant colony and the 3D matrix method is developed to determine the optimal dimensions and the main influencing performances of fundamental analog circuits and, including operational amplifiers (op-amps). The proposed methodology uses ACO and Cadence Spectre as dimensioning techniques and implementation platform, respectively. The Three Dimensions (3D) ACO algorithm is successfully used for analog circuit sizing, and the obtained results demonstrate its effectiveness in sizing basic and more complicated analog circuits.

Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.

Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection

Continuous ant colony optimization was a population-based heuristic search algorithm inspired by the pathfinding behavior of ant colonies with a simple structure and few control parameters. However, in the case of multimodal and high-dimensional optimization problems, it was often limited to local regions in the feasible domain space, negatively affecting the computational effort required to find the optimal solution point. To alleviate its limitations in this regard, a random following strategy is proposed to enhance communication among the ant colony search agent and other ant colony members within the search dimension. The proposed algorithm that incorporates this strategy is called Random Following Ant Colony Optimization. Then, to evaluate the global optimization performance of the proposed algorithm, the well-known numerical optimization problem, namely the Congress on Evolutionary Computation 2017 test suite, is used. First, the proposed algorithm’s parameters are analyzed for sensitivity, scalability experiments, and balanced diversity. Second, it is compared experimentally with 11 state-of-the-art algorithms in dimensions 10, 30, 50, and 100, respectively, and Wilcoxon signed-rank test, Friedman test, and Bonferroni-Dunn post-hoc statistical test are used to synthesize the experimental comparison results. Finally, to evaluate the ability of the proposed algorithm to handle discrete feature selection problems, comparative experiments are conducted on 24 datasets with eight well-known classification methods and five high-performance classification methods. The benchmark test results show that the global optimization performance of the proposed algorithm is comparable to the winners of the test suite in 50 and 100 dimensions. The results of the feature selection experiments show that the proposed algorithm is much stronger than the well-known and high-performance classification methods on high-dimensional datasets.

Examining the Social Behavior of Ant Colonies Using Complex Networks

"This paper proposes the use of Complex Network Theory to model the interactions between ants and analyze their social behavior. Specifically, the study focuses on six colonies of ants to investigate whether their behavior is community-oriented or individual-oriented. The research employs various nodes properties that define nodes’ importance to quantify the existence of a social or individual-oriented behavior. The results aim to provide insights into the social behavior of ants and may have implications for understanding other complex social systems. Keywords: complex networks, centrality measurements, social behaviours. "

A hybrid of ant colony optimization, genetic algorithm and flux balance analysis for optimization of succinic acid production in Escherichia coli

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization–genetic algorithm–flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset [Formula: see text]JR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli.

Temporal Design of Art Painting Landscape Based on Ant Colony Optimization Algorithm

In the long history of painting, art research in each period will also reflect on the current situation and future improvement. Inspired by the group behavior of natural insect communities, to find the shortest path between insect nests and food, a simulated evolutionary algorithm ant colony algorithm is proposed to simulate the foraging behavior of natural ant colonies. This paper studies the temporal design based on ant colony optimization algorithm. Time is an inseparable part of landscape design. With the continuous progress of time, various landscape characteristics have also been formed. The purpose of this paper is to provide theoretical and practical support for the improvement of art painting landscape design from the perspective of time. The ant colony optimization algorithm is used to analyze and analyze the values of the algorithm parameters.

Olfactory sensitivity differentiates morphologically distinct worker castes in Camponotus floridanus

BackgroundCamponotus floridanus ant colonies are comprised of a single reproductive queen and thousands of sterile female offspring that consist of two morphologically distinct castes: smaller minors and larger majors. Minors perform most of the tasks within the colony, including brood care and food collection, whereas majors have fewer clear roles and have been hypothesized to act as a specialized solider caste associated with colony defense. The allocation of workers to these different tasks depends, in part, on the detection and processing of local information including pheromones and other chemical blends such as cuticular hydrocarbons. However, the role peripheral olfactory sensitivity plays in establishing and maintaining morphologically distinct worker castes and their associated behaviors remains largely unexplored.ResultsWe examined the electrophysiological responses to general odorants, cuticular extracts, and a trail pheromone in adult minor and major C. floridanus workers, revealing that the repertoire of social behaviors is positively correlated with olfactory sensitivity. Minors in particular display primarily excitatory responses to olfactory stimuli, whereas major workers primarily manifest suppressed, sub-solvent responses. The notable exception to this paradigm is that both minors and majors display robust, dose-dependent excitatory responses to conspecific, non-nestmate cuticular extracts. Moreover, while both minors and majors actively aggress non-nestmate foes, the larger and physiologically distinct majors display significantly enhanced capabilities to rapidly subdue and kill their adversaries.ConclusionsOur studies reveal the behavioral repertoire of minors and majors aligns with profound shifts in peripheral olfactory sensitivity and odor coding. The data reported here support the hypothesis that minors are multipotential workers with broad excitatory sensitivity, and majors are dedicated soldiers with a highly specialized olfactory system for distinguishing non-nestmate foes. Overall, we conclude that C. floridanus majors do indeed represent a physiologically and behaviorally specialized soldier caste in which caste-specific olfactory sensitivity plays an important role in task allocation and the regulation of social behavior in ant colonies.

A dataset of ant colonies' motion trajectories in indoor and outdoor scenes to study clustering behavior.

The motion and interaction of social insects (such as ants) have been studied by many researchers to understand clustering mechanisms. Most studies in the field of ant behavior have focused only on indoor environments (a laboratory setup), while outdoor environments (natural environments) are still underexplored. In this article, we collect 10 videos of 3 species of ant colonies from different scenes, including 5 indoor and 5 outdoor scenes. We develop an image sequence marking software named VisualMarkData, which enables us to provide annotations of the ants in the videos. (i) It offers comprehensive annotations of states at the individual-target and colony-target levels. (ii) It provides a simple matrix format to represent multiple targets and multiple groups of annotations (along with their IDs and behavior labels). (iii) During the annotation process, we propose a simple and effective visualization that takes the annotation information of the previous frame as a reference, and then a user can simply click on the center point of each target to complete the annotation task. (iv) We develop a user-friendly window-based GUI to minimize labor and maximize annotation quality. In all 5,354 frames, the location information and the identification number of each ant are recorded for a total of 712 ants and 114,112 annotations. Moreover, we provide visual analysis tools to assess and validate the technical quality and reproducibility of our data. We provide a large-scale ant dataset with the accompanying annotation software. It is hoped that our work will contribute to a deeper exploration of the behavior of ant colonies.

An Ant Colony Optimization Behavior-Based MOEA/D for Distributed Heterogeneous Hybrid Flow Shop Scheduling Problem Under Nonidentical Time-of-Use Electricity Tariffs

This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs (DHHFSP-NTOU). The makespan and the total electricity charge are considered as the optimization objectives from the view of production and management. The DHHFSP-NTOU considers different processing capabilitie and time-of-use electricity tariffs for each factory. The mixed-integer linear programming (MILP) model of DHHFSP-NTOU is established. To solve the DHHFSP-NTOU, this article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D). A problem-specific ant colony behavior is presented to construct offspring individuals. Eight neighborhoods within the factory and between factories are adopted to improve the quality of the individuals in the archive set. A right-shift movement is used to reduce the electricity charge. A large number of numerical experiments and comprehensive investigations are carried out to test the efficiency and effectiveness of ACO_MOEA/D. The experimental results show that each component (e.g., ant colony behavior, neighborhoods move operators, right-shift movement) contributes to the performance of ACO_MOEA/D. The comparisons with several related algorithms show the superiority of ACO_MOEA/D for solving the DHHFSP-NTOU. Note to Practitioners—From the managers’ insights, the electricity charge is a large cost in the production. The scheduling is an economical approach to reduce the electricity charge. For the time-of-use (TOU) tariffs, the managers can adjust the schedule to reduce the idle time or move some operations to the interval period with a lower electric price. This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical TOU (UTOU) electricity. This model can be used in many manufacturing enterprises that have several heterogeneous factories. This article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D) to minimize the makespan and the total electricity charge. The ACO_MOEA/D can provide the economy and high-efficiency schedules for practitioners. The computational results confirm its effectiveness and efficiency.

Adaptive gradient descent enabled ant colony optimization for routing problems

The design of Ant Colony Optimization (ACO) has been inspired by the foraging behavior of ant colonies. ACO is one of the most widely used metaheuristic algorithms applicable to various optimization problems. Nevertheless, the deficiency of ACO is early maturity and slow convergence, usually leading to dissatisfactory performance. In this research, a new type of ACO is proposed with innovative adaptive learning mechanism is devised (the algorithm is called ADACO). In the paper, first, the ACO algorithm for Traveling Salesman Problem (TSP) is modeled in the framework of reinforcement learning (RL) and learns a best policy by stochastic gradient descent (SGD). Then, an adaptive gradient descent strategy, which can exploit the update history of per-dimensional pheromones to achieve intelligent convergence, is integrated into the ACO algorithm as ADACO. A parallel computation process is implemented in the process to expedite computational efficiency. Finally, through case studies in TSP and Capacitated Vehicle Routing Problem (CVRP), ADACO is validated. ADACO is trialed on various sizes of TSP and CVRP instances and compared with other state-of-the-art algorithms. Results show that ADACO is competitive in terms of accuracy (better in most cases), stability (statistically significant) and adaptability (support various types of problems). The results also elucidate that the algorithm maintains good computational efficiency owing to its parallel implementation. It can be concluded that ADACO is effectively applicable to routing problems with good performance.

Application of the ant colony algorithm for solving the job shop problem

The ant colony algorithm was first introduced by M. Dorigo, V. Maniezzo and A. Colorni in 1991. This algorithm is based on the behavior of a real ant colony when searching for food. The main area of application of the algorithm is the problem of combinatorial optimization. This paper describes the implementation of the algorithm, setting the parameters and considers its effectiveness in solving the job shop problem.

Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency

In Spatial crowdsourcing, mobile users perform spatio-temporal tasks that involve travel to specified locations. Spatial crowdsourcing (SC) is enabled by SC platforms that support mobile worker recruitment and retention, as well as task assignment, which is essential to maximize profits that are accrued from serving task requests. Specifically, how to best achieve task assignment in a cost-effective manner while contending with spatio-temporal constraints is a key challenge in SC. To address this challenge, we formalize and study a novel Profit-driven Task Assignment problem. We first establish a task reward pricing model that takes into account the temporal constraints (i.e., expected completion time and deadline) of tasks. Then we adopt an optimal algorithm based on tree decomposition to achieve an optimal task assignment and propose greedy algorithms based on Random Tuning Optimization to improve the computational efficiency. To balance effectiveness and efficiency, we also provide a heuristic task assignment algorithm based on Ant Colony Optimization that assigns tasks by simulating behavior of ant colonies foraging for food. Finally, we conduct extensive experiments using real and synthetic data, offering detailed insight into effectiveness and efficiency of the proposed methods.

Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia)

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.

Arts of Living on a Damaged Planet: Ghosts and Monsters of the AnthropoceneAfter Extinction

Arts of Living on a Damaged Planet: Ghosts and Monsters of the AnthropoceneAfter Extinction

A Joint Tracking Approach via Ant Colony Evolution for Quantitative Cell Cycle Analysis.

In this paper, we use an ant colony heuristic method to tackle the integration of data association and state estimation in the presence of cell mitosis, morphological change and uncertainty of measurement. Our approach first models the scouting behavior of an unlabeled ant colony as a chaotic process to generate a set of cell candidates in the current frame, then a labeled ant colony foraging process is modeled to construct an interframe matching between previously estimated cell states and current cell candidates through minimizing the optimal sub-pattern assignment metric for track (OSPA-T). The states of cells in the current frame are finally estimated using labeled ant colonies via a multi-Bernoulli parameter set approximated by individual food pheromone fields and heuristic information within the same region of support, the resulting trail pheromone fields over frames constitutes the cell lineage trees of the tracks. A four-stage track recovery strategy is proposed to monitor the history of all established tracks to reconstruct broken tracks in a computationally economic way. The labeling method used in this work is an improvement on previous techniques. The method has been evaluated on publicly available, challenging cell image sequences, and a satisfied performance improvement is achieved in contrast to the state-of-the-art methods.

Cryptanalysis of Merkle-Hellman cipher using ant colony optimization

<p class="Abstract"><span id="docs-internal-guid-d3fe8e21-7fff-17fc-df0e-00893428243c"><span>The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).</span></span></p>