Showing papers on "Ant colony published in 2019"

Nestmate Recognition in Ants

Abstract: All ants are highly eusocial, which means individuals care for the young, there are castes, and there is an overlap of at least two generations in which workers assist their mother in rearing sisters and brothers. Ants have developed a formidable array of active and /or passive semiochemical and non-chemical defenses. Knowledge of nestmate recognition is essential for a comprehensive understanding of both ant defenses and the organisms that have broken the recognition code and are able to infiltrate ant colonies and exploit colony resources. The simplest ant colony situation is one where there is a single queen, inseminated by a single male, the colony resides in a single nest and workers from each colony defend a territory. In all ant species nestmate recognition discriminators and the neural template are derived to some extent from all possible sources -- the environment, the individual, class of worker, or queen from within the colony, or collectively from all individuals in a colony.

Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach

Abstract: Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

Path Planning for Autonomous Underwater Vehicles: An Ant Colony Algorithm Incorporating Alarm Pheromone

Abstract: Path planning is a critical issue to ensure the safety and reliability of the autonomous navigation system of the autonomous underwater vehicles (AUVs). Due to the nonlinearity and constraint issues, existing algorithms perform unsatisfactorily or even cannot find a feasible solution when facing large-scale problem spaces. This paper improves the path planning of AUVs in terms of both the path planning model and the optimization algorithm. The proposed model is comprehensive, which aggregates the length, energy consumption, and collision risk into the objective function and incorporates the steering window constraint. Based on the model, we develop a nature-inspired ant colony optimization algorithm to search the optimal path. Our algorithm is named alarm pheromone-assisted ant colony system (AP-ACS), since it incorporates the alarm pheromone in addition to the traditional guiding pheromone. The alarm pheromone alerts the ants to infeasible areas, which saves invalid search efforts and, thus, improves the search efficiency. Meanwhile, three heuristic measures are specifically designed to provide additional knowledge to the ants for path planning. In the experiments, different from the previous works that are tested on synthetic instances only, we implement an interface to retrieve the practical underwater environment data. AP-ACS and the compared algorithms are thus tested on several practical environments of different scales. The experimental results show that AP-ACS can effectively handle the constraints and outperforms the other algorithms in terms of accuracy, efficiency, and stability.

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

Abstract: This paper presents an efficient double-layer ant colony optimization algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms that run independently and successively. First, a parallel elite ant colony optimization method is proposed to generate an initial collision-free path in a complex map, and then, we apply a path improvement algorithm called turning point optimization algorithm, in which the initial path is optimized in terms of length, smoothness, and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.

Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

Abstract: A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive online and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an online, bio-inspired approach to clustering dynamic data streams. The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behavior of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.

A Method Based on the Combination of Laxity and Ant Colony System for Cloud-Fog Task Scheduling

Abstract: In today's Internet of Things research community, Cloud-fog framework is a potential technology for Internet of Things to support energy consumption of an IoT system and delay-sensitive applications that require almost real-time responses. However, how to schedule the computational tasks which is to offload to fog nodes or cloud nodes is not fully addressed until now. In this paper, in order to solve the complex task scheduling problem with some priority constraints of IoT applications taking into account the energy consumption and reducing energy consumption on the condition of satisfying the mix deadline, we formulate an associated task scheduling problem into a constrained optimization problem in cloud-fog environment. A laxity and ant colony system algorithm(LBP-ACS) is put forward to tackle this problem. In this algorithm, a strategy of task scheduling is not only considering the priority of a task, but also its finished deadline. In order to handle the sensitivity of task delay, the laxity-based priority algorithm is adopted to construct a task scheduling sequence with reasonable priority. Meanwhile, to minimize the total energy consumption, the constrained optimization algorithm based on ant colony system algorithm is used to obtain the approximate optimal scheduling scheme in the global. Compared with other algorithms, the experimental results show that the proposed algorithm can effectively reduce the energy consumption of processing all tasks, while ensuring reasonable scheduling length and reducing the failure rate of associated tasks scheduling with mixed deadlines.

Numerical Optimization of the Energy Consumption for Wireless Sensor Networks Based on an Improved Ant Colony Algorithm

Abstract: In the current WSN operation process, there are two major problems of data collection difficulty and network energy consumption, which seriously affects the reliability of the WSN. In this paper, the improved ant colony algorithm proposed is compared with other algorithms. Wireless sensor network nodes based on improved ant colony algorithm have lower energy consumption, and sensor nodes have more residual energy. In addition, the energy model, data transmission balance model is established and verified in the WSN transmission target function. The experimental results show that the WSN node after the improved ant colony algorithm is used to help in the determination of the location information of the public node, and then use the location information of the node to make the protocol have effective routing performance and effective target node location discrimination ability. Thus, the improved ant colony algorithm studied in this paper has important practical significance for improving the life cycle/energy consumption of wireless sensor networks. In addition, aiming at the characteristics and routing performance of wireless sensor networks, a low-power routing method based on location and direction is designed to make the message reach the target node accurately and safely, which effectively increases the data packet transmission rate.

Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient

Abstract: Ant Colony Optimization algorithms have been successfully applied to solve the Traveling Salesman Problem (TSP). However, they still have a tendency to fall into local optima, mainly resulting from poor diversity, especially in those TSPs with a lot of cities. To address this problem, and further obtain a better result in big-scale TSPs, we propose an algorithm called Multiple Colonies Ant Colony Optimization Based on Pearson Correlation Coefficient (PCCACO). To improve the diversity, first, we introduce a novel single colony termed Unit Distance-Pheromone Operator, which along with two other classic ant populations: Ant Colony System and Max-Min Ant System, make the final whole algorithm. A Pearson correlation coefficient is further employed to erect multi-colony communication with an adaptive frequency. Besides that, an initialization is applied when the algorithm is stagnant, which helps it to jump out of the local optima. Finally, we render a dropout approach to reduce the running time. The extensive simulations in TSP demonstrate that our algorithm can get a better solution with a reasonable variation.

Meta-heuristic Ant Colony Optimization Based Unequal Clustering for Wireless Sensor Network

Abstract: Sensor nodes are randomly deployed to perform specific area monitoring in geographical region and temporal space. The network connectivity maintenance is a major requirement for accurate event detection with minimum energy consumption. To minimize the energy consumption, various clustering algorithms have been evolved in research studies. But, they failed to consider the other performance parameters such as quality of service constraints and the performance level. The initialization of nodes nearer to the base station (BS) as relay nodes reduces the number of relay node participation and increases the performance. This paper proposes the novel ant colony meta-heuristic based unequal clustering for the novel cluster head (CH) selection. The data fusion from the CH node to the intermediate node called Rendezvous node reduces the message transmissions and hence the energy consumed by the nodes is minimum. The neighbor finding phase and the link maintenance through the Meta-Heuristic Ant Colony Optimization approach selects the optimal path between the nodes which increases the packets delivered to the destination. The population initialization requires more time at this stage. Hence, the Haversine distance is estimated among the nodes which also reduces the dimensionality of the message transmission among the nodes. The prediction of optimal path and the CH selection using Ant Colony Optimization Meta-Heuristic and unequal clustering reduces the energy consumption effectively. The comparative analysis of proposed Meta-Heuristic Ant Colony Optimization based Unequal Clustering with the existing unequal clustering approaches on the basis of various performance parameters such as Packet Delivery Ratio, number of packets sent to the BS, energy consumption, residual energy and the percentage of dead nodes shows the effectiveness of proposed work in WSN applications.

Collision Avoidance Effects on the Mobility of a UAV Swarm Using Chaotic Ant Colony with Model Predictive Control

Abstract: The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics.

Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

Abstract: For the problem of mobile robot's path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.

Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM

Abstract: In this paper, a least squares support vector machine (LSSVM) model with parameter optimization is proposed for solving the problem that the forecast accuracy of neural network model and support vector machine model is not desirable for the sake of improving short-term wind speed forecast accuracy further. The parameters of LSSVM are optimized by the improved ant colony algorithm. Firstly, the parameters of LSSVM are regarded as the position vector of ants. Another argument is that the global search is carried out by selecting some ants randomly from the ant colony to guide the whole ant colony, while searching the optimal ant neighborhood. Furthermore, the optimal parameters of the model are obtained, and the wind speed prediction model of LSSVM is established through parameter optimization. Taking a wind farm in North China as an example, the collected wind speed data were taken in predicted experience, besides the results were compared with the BP neural network model and the LSSVM model. The results show that this model has significant advantages compared with the other two models and has high practical significance.

Ant colony optimisation

Abstract: Ant Colony Optimisation (ACO) is one of the well-known swarm intelligence techniques in the literature. This chapter discusses the inspiration and mathematical model of several valiants of this algorithm. To analyse the performance of ACO, it is applied to several Travailing Salesman Problem (TSP).

Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

Abstract: Ant Colony Optimization has achieved good results in solving Traveling Salesman Problem (TSP), it has a tendency to fall into local optima and the convergence speed is limited. To address this problem, multi-colony ant colony optimization based on the generalized Jaccard similarity recommendation strategy (JCACO) is proposed. Firstly, two classical ant populations, Ant Colony System and Max-Min Ant System are selected to form heterogeneous multi-colony. Secondly, attribute-based collaborative filtering recommendation mechanism is proposed to balance the diversity and convergence of the algorithm, three strategies have been implemented under this recommendation mechanism: The attribute cross-learning strategy is used to highlight the effect of excellent attributes and improve the attribute comprehensive performance; According to the diversity results of the population measured by information entropy, the attribute recommendation learning strategy is used to enrich the diversity of the population adaptively; The pheromone reward strategy is implemented on the public path to accelerate the convergence speed; Among which, according to the generalized Jaccard similarity coefficient, the most suitable communication object is recommended in order to achieve the best learning efficiency. Finally, when the algorithm stagnates, the elite reverse learning mechanism is used to jump out of the local optimum. Experimental results show that JCACO has good performance and high stability in TSP instances, especially in large-scale TSP instances.

A novel dynamic assignment rule for the distributed job shop scheduling problem using a hybrid ant-based algorithm

Abstract: Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NP-hard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees near-optimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the ant-based algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from well-known classical job-shop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others.

UAV Path Planning for Data Gathering of IoT Nodes: Ant Colony or Simulated Annealing Optimization

Abstract: Using UAVs is a promising solution for gathering information of the wireless IoT sensors in geographic areas. In this UAVs mission, due to battery-powered, the shortest possible path between sensors should be found. In this paper, two optimization methods including ant colony algorithm and simulated annealing algorithm are modeled in three-dimensional mode to compare the performance and execution time of these two methods in different size of sensors. The results shows the SA optimization can be performed faster than an ant colony optimization for benchmarks in which the number of sensors is less than 50.

Evolution of dispersal in ants (Hymenoptera: Formicidae): a review on the dispersal strategies of sessile superorganisms

Abstract: The extreme diversity of dispersal strategies in ants is unique among terrestrial animals. The nature of ant colonies as social, perennial and sessile superorganisms is the basis for understanding this diversity, together with the inclusive fitness framework for social evolution. We review ant dispersal strategies, with the aim of identifying future research directions on ant dispersal and its evolution. We list ultimate and proximate determinants of dispersal traits and the ecological and evolutionary consequences of dispersal for population structures and dynamics, as well as species communities. We outline the eco-evolutionary feedbacks between the multitude of traits affecting dispersal evolution, and the likely evolutionary routes and ecological drivers in transitions among the diverse ant dispersal strategies. We conclude by presenting a research framework to fill the gaps in current knowledge: including comparative studies of colony life histories and population structures, and theoretical models of the eco-evolutionary dynamics affecting dispersal, in an inclusive fitness framework. Open access, licensed under CC BY 4.0. © 2018 The Author(s) DOI: https://doi.org/10.25849/myrmecol.news_029:035 Pages: 35-55 Volume: 29 Year: 2019 Journal: Myrmecol. News

Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing

Abstract: This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.

Ant colony system with a novel Non-DaemonActions procedure for multiprocessor task scheduling in multistage hybrid flow shop

Abstract: This paper presents an ant colony system with a novel Non-DaemonActions procedure (ACSNDP) algorithm for multiprocessor task scheduling in multistage hybrid flow shop. A DaemonActions procedure is an optional component of ant colony optimization (ACO), which integrates problem-specific actions that cannot be performed by single ants such as the actions performed by local search routines. In many applications to hard combinatorial optimization problems, ACO performs best when integrated with local search routines because they move the ants' solutions to their local optimums. However, such an integration is shown as an effective approach only experimentally, and does not have any effects on the convergence properties of ACO theoretically, since the validity of convergence proofs depends only on the way solutions are constructed and not on the fact that the solutions are moved or not to their local optimums. Furthermore, it can be noticed that the traditional DaemonActions procedure does not interfere in the way solutions are constructed because local search routines have been always integrated with ACO in a daemon fashion i.e. they have been made hidden to the ants, and the ants do not how their solutions have been relocated. Consequently, the ants may perform limited exploitation (intensification) because they cannot exploit the problem-specific knowledge brought by the local search routine in such a way that enables them to construct these local optimums by themselves in the upcoming tour. In order to overcome these limitations, a novel Non-DaemonActions procedure, which can interfere positively in the way solutions are constructed, is proposed as follows. Iteratively at the end of each tour, the local search routine tries to improve the constructed solution, and then if a local optimum has been found, the ant learns the modifications made by the local search routine on its solution, and performs the corresponding modifications in the pheromone concentrations and heuristic information that will probably enable it to construct this local optimum by itself, in the upcoming tour, before the application of the local search over again. If the ant can do that, it will be able to construct more accurate local optimums in the upcoming tours by repeating the whole process over and over again, and thus enhance its exploitation capabilities. The proposed algorithm is tested on 700 well-known benchmark instances, with the proposed Non-DaemonActions procedure, and without it using the classical alternatives, and also compared with other 12 algorithms well-known in the literature. Computational results verify the improvements achieved by the proposed procedure, and show the superiority of the proposed algorithm over 7 of the compared works in terms of solution quality.

Testing the limits of pheromone stigmergy in high-density robot swarms

Abstract: Area coverage and collective exploration are key challenges for swarm robotics. Previous research in this field has drawn inspiration from ant colonies, with real, or more commonly virtual, pheromo...

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

Abstract: 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.

Ant Colony Algorithm for Routing Alternate Fuel Vehicles in Multi-depot Vehicle Routing Problem

Abstract: A Multi-depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. An Ant Colony System-based metaheuristic is proposed to find the solution to this problem. The solution for MDGVRP is useful for companies, who employ the Alternative Fuel-Powered Vehicles (AFVs) to deal with the obstacles brought by the limited number of the Alternative Fuel Stations. This paper adds an important constraint, vehicle capacity to the model, to make it more meaningful and closer to real-world case. The numerical experiment is performed on randomly generated problem instances to understand the property of MDGVRP and to bring the managerial insights of the problem.

An improved ant colony optimization algorithm and its applications to limit analysis of frame structures

Abstract: This article presents a modified ant colony systems approach, which allows reduction of the computational effort needed to converge to the optimal solution of a given engineering problem. The propo...

Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)

Abstract: The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.

An Otsu multi-thresholds segmentation algorithm based on improved ACO

Abstract: For the traditional multi-thresholds segmentation algorithms, usually it would take too much time in finding the optimal solution. As one of the widely used swarm-intelligence optimization algorithms, ant colony optimization (ACO) algorithm has been introduced to optimize the thresholding search process. The traditional ACO is improved in this paper to get a faster convergence speed and applied in Otsu multi-thresholds segmentation algorithms. When the ant colony is initialized, each member of the ant colony is distributed evenly in the solution space, so that it could search the entire solution space as fast as possible. In the search process, the random step length of ants moving is generated by the Levy flight pattern, but the global transition probability of the traditional ACO is used to control the search range of the ant colony. The experimental results show that the proposed algorithm could obtain the optimal thresholds faster and more effectively than the traditional Otsu algorithm and the Otsu based on traditional ACO.

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

Abstract: The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non‐Glioma image based on the optimized set of features. Furthermore, energy‐based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.

Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks

Abstract: Community detection aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. The problem can be modeled as an NP hard combinatorial optimization problem, to which multi-objective optimization has been applied, addressing the common resolution limitation problem in modularity-based optimization. In the literature, ant colony optimization (ACO) algorithm, however, has been only applied to community detection with single objective. This is due to the main difficulties in defining and updating the pheromone matrices, constructing the transition probability model, and tuning the parameters. To address these issues, a multi-objective ACO algorithm based on decomposition (MOACO/D-Net) is proposed in this paper, minimizing negative ratio association and ratio cut simultaneously in community detection. MOACO/D-Net decomposes the community detection multi-objective optimization problem into several subproblems, and each one corresponds to one ant in the ant colony. Furthermore, the ant colony is partitioned into groups, and ants in the same group share a common pheromone matrix with information learned from high-quality solutions. The pheromone matrix of each group is updated based on updated nondominated solutions in this group. New solutions are constructed by the ants in each group using a proposed transition probability model, and each of them is then improved by an improvement operator based on the definition of strong community. After improvement, all the solutions are compared with the solutions in the external archive and the nondominated ones are added to the external archive. Finally each ant updates its current solution based on a better neighbor, which may belong to an adjacent group. The resulting final external archive consists of nondominated solutions, and each one corresponds to a different partition of the network. Systematic experiments on LFR benchmark networks and eight real-world networks demonstrate the effectiveness and robustness of the proposed algorithm. The ranges of proper values for each parameter are also analyzed, addressing the key issue of parameter tuning in ACO algorithms based on a large number of tests conducted.

Cloud service composition using an inverted ant colony optimisation algorithm

Abstract: In recent years, clouds are becoming an important platform for scientific applications. Service composition is a growing approach that increases the number of applications of cloud computing by reusing attractive services. However, more available approaches focus on producing composite services from a single cloud, limiting the benefits derived from other clouds. Furthermore, in many traditional service composition methods, there is a key problem called load balancing that was inefficient among cloud servers. Therefore, this paper proposes the inverted ant colony optimisation (IACO) algorithm, a variation of the basic ant colony optimisation (ACO) algorithm, to solve this problem. This method inverts its logic by converting the effect of pheromone on the selected path by ants in order to improve load balancing among cloud servers. In this method, ants begin to traverse the graph from the start node and each ant selects the best node for moving, then other ants may not follow the track travelled by the previous ants. We evaluate the performance of the proposed method in comparison with the ACO, greedy and COM2 algorithms in terms of the obtained optimal cloud composition, load balancing, waiting time, cost and execution time.

A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment

Abstract: In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR's improvement in performance.