Pheromone Table Research Articles

Knowledge-driven ant colony optimization algorithm for vehicle routing problem in instant delivery peak period

Instant delivery is an important part of urban logistics distribution, which realizes point-to-point distribution between merchants and customers. During the peak period of orders, instant delivery is a large-scale variable NP-hard combinatorial optimization problem, which increases the difficulty and complexity of scheduling greatly. To solve the large-scale vehicle routing problem of instant delivery in peak periods, a knowledge-driven ant colony optimization (KDACO) algorithm is proposed in this paper. First, the knowledge base is established to guide evolutionary search, including the knowledge of order priority and the feature knowledge of feasible schemes. Second, the pheromone supplementation strategy is designed based on the knowledge of order priority, enhancing the guiding ability of the pheromone table. Third, the adaptive evolutionary operator is designed based on the feature knowledge of feasible schemes, improving the optimization efficiency of the algorithm. Finally, numerical experiments on extensive classical datasets show that the proposed KDACO can obtain superior performance to other state-of-the-art algorithms in the instant delivery peak period.

Using The Hybrid GA-Ant Algorithm To Find The Optimal Path In Computer Networks

Cost management is one of the performance standards in computer networks and routing strategies through which we can get effective paths in the computer network, reach the target and perform highly in the network by improving the routing table (jumps). This paper is an attempt to propose a new H design mixed algorithm (ACO-GA) that includes the best features of both ACO and GA with a new application that combines both previous algorithms called( H- Hybrid (ACO-GA) hybrid algorithm technology, which differs in its parameters. In order to research and find the optimal path, the improved ant algorithm was used to explore the network, using smart beams, getting the paths generated by ants and then using them as inputs into the genetic algorithm in the form of arranged pairs of chromosomes. Experimental results through extensive simulations showed that H (ACO-GA) improves the routing schedule, represented by the pheromone values that ants leave when following their path in the network. The values given in the table( 3.2) vary according to the quality of the pheromone concentration. In this case, it is possible to give the greatest opportunity to choose the best quality according to the concentration of the pheromone. For this purpose, a network consisting of four nodes (1), (2), (3), (4) was used starting with node (1) which is the source node and the destination node (2), by calling the selection technique to update the pheromone table by choosing the path to node (1 ). For this case and for selecting the destination node (2), the pheromone table for the nodes visited by the ant is updated. We calculated the final destination )2) by dividing the ratio. Thus, we get to reduce the search area, speed up search time, and improve the quality of the solution by obtaining the optimum set of paths.

Population Diversity in Ant-inspired Optimization Algorithms

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of its automatic detection and counteracting are of great importance. Measuring diversity in Evolutionary Algorithms working in real-value search space is often computationally complex, but feasible while measuring diversity in combinatorial domain is practically impossible (cf. Closest String Problem). Nevertheless, we propose several practical and feasible diversity measurement techniques dedicated to Ant Colony Optimization algorithms, leveraging the fact that even though analysis of the search space is at least an NP problem, we can focus on the pheromone table, where the direct outcomes of the search are expressed and can be analyzed. Besides proposing the measurement techniques, we apply them to assess the diversity of several variants of ACO, and closely analyze their features for the classic ACO. The discussion of the results is the first step towards applying the proposed measurement techniques in auto-adaptation of the parameters affecting directly the exploitation and exploration features in ACO in the future.

Implementation of ACO in Vanet with Detection of Faulty Node

Objective: To propose an Ant Colony Optimization (ACO) based routing approach for estimating the reliability parameter of backward and forward ants. Methods: The ACO-based proposed methodology is used to predict path or route discovery in VANET. This study has discovered the best optimal path for data transmission. The forward ant movement has a minimum hop count of four for vehicle number 6. The vehicle 6 is a source vehicle, and it starts data transmission. The pheromone concentration of the entire vehicle defers and, after updating, the performance was analyzed. The best path is selected based on hop count, then a path with maximum pheromone concentration is selected. The pheromone evaporation factor is updated after a certain time interval. Findings: We have checked the reliability of the path, considering pheromone evaporation as a factor, the pheromone table is updated at a regular time interval, and the reliability parameter is also proposed for performing a task without failure in an efficient manner. We have arranged a routing table in descending order of hop count and then select the best of three paths with a minimum hop count. We search for hop count less than or equal to 5 and total pheromone greater than or equal to 17.55. We find that Ant 3 having a hop count of 5 and total pheromone concentration of 19.37. In this work, we have taken the evaporation factor as 0.5. If the transmission of a message is successful, then the reliability assigned to the edge will be 100, and for unsuccessful transmission, reliability is taken down by 20. If the reliability of the path will become 40, then the vehicle has to search for a new path. In this way, we eliminate malicious nodes for efficient transmission of message. Novelty: The performance of VANET is improved using ACO based proposed routing approach. The optimal path is selected with a minimum hop count and reliability parameter to identify the successful and unsuccessful path. Keywords: Network security; VANET; Sybil; ACO; Pheromone Evaporation

Ant colony optimization–evolutionary hybrid optimization with translation of problem representation

AbstractDifferent hybrid optimization metaheuristics (see the works of Talbi for classification) either assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a local search inside an evolutionary algorithm—a memetic algorithm) or creating a chain of algorithms. In this paper, such a chain combination of two algorithms (namely, the Ant Colony Optimization and Evolutionary Algorithm) is presented. However, because of the intrinsic differences between the two algorithms (a vector of labels and a pheromone table when solving the traveling salesman problem, for example), several dedicated algorithms for translating the solutions between these two representations of the problem are proposed. The hybrid algorithm constructed with the application of the translation methods turns out to be significantly better in solving the TSP compared to non‐hybrid versions (relevant experimental results are presented and discussed). This paves the way for new possibilities of constructing hybrid metaheuristics by putting together completely different ones (using different representations); the impact of the presented research is aimed far beyond the hybridization of only ant colony optimization and evolutionary algorithm.

Multi-path data fusion method based on routing algorithm for wireless sensor networks

ABSTRACT For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

Improving the Area Efficiency of ACO-Based Routing by Directional Pheromone in Large-Scale NoCs

Ant Colony Optimization (ACO) is a distributed collective-intelligence algorithm. Several adaptive routing algorithms based on ACO have been proposed in the domain of Network-on-Chip (NoC) design for balancing traffic load. However, when network size becomes large, the conventional ACO requires quite a lot of pheromones for predicting network load distribution, which results in large hardware cost and low cost-efficiency. In this paper, an ACO algorithm with directional pheromone (ACO-DP) is proposed for reducing the size of pheromone table in large-scale networks. Moreover, by using a distance-sensitive backward pheromone updating scheme, the performance of ACO-DP is also improved. Finally, we introduce the detailed architecture and hardware implementation of ACO-DP routing. Experimental results show that ACO-DP routing achieves the highest area efficiency in large-scale NoC systems compared to other ACO-based routing algorithms.

A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

Provident Routing Scheme Rely on Swarm Astuteness for MANET

Mobile Ad-Hoc Network dynamically changes its topology and also it doesn’t have proper infrastructure. So, it is necessary to provide the route stability. Already several routing methodologies are available for MANET. But those are not take the delay occurs in the network into account. As the topology of the network is dynamically changed, the route failure occurs frequently. Alternate route should be selected to route the data to its destination. The alternate route may be shortest path or it may not be. So, the concept of swarm astuteness is included in order to route the data reliably and quickly. The hill building behavior of termite inspires to find the alternate shortest path in the dynamic environment. In biological context, the termites are using pheromone to find out the path to reach the destination. Similarly, in this paper the pheromone table is maintained for each node. The MANET uses the hill building behavior of termite to discover the route dynamically which provides better packet delivery and decreases the control overhead.

Implementasi Algoritma MAX-MIN Ant System pada Penjadwalan Mata Kuliah

University Course Timetabling Problem is a problem faced by every university, one of which is Universitas Multimedia Nusantara. Timetabling process is done by allocating time and space so that the whole associated class and course can be implemented. In this paper, the problem will be solved by using MAX-MIN Ant System Algorithm. This algorithm is an alternative approach to ant colony optimization. This algorithm uses two tables of pheromones as stigmergy, i.e. timeslot pheromone table and room pheromone table. In addition, the selection of timeslot and room is done by using the standard deviation of the value of pheromones. Testing is carried out by using 105 events, 45 timeslots, and 3 types of categories based on the number of rooms provided, i.e. large, medium, and small. In each category, testing is performed 5 times and for each testing, the data recorded is the unplace and Soft Constraint Penalty. In general, the greater the number of rooms, the smaller the unplace.
 Index Terms—ant colony optimization, max-min ant system, timetabling

Pheromone-Based Crossover Operator Applied to Distribution System Reconfiguration

This paper proposes an algorithm based on hybridization of pheromones derived from ant colony optimization with crossover operator and is applied to minimize real losses in distribution systems using reconfiguration. The algorithm maintains a population of candidate solutions and a table of pheromones to reinforce better edges during search process. Exploration for better solutions is performed by means of crossover operator directed by the information stored in the pheromone table. The hybrid approach has been successfully implemented on three test networks. The efficacy of the algorithm has been demonstrated by presenting a comparison between results obtained using uniform crossover operator and pheromone directed crossover and also by comparing with the results published in the literature for the test systems considered.

Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering

Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequence approach with an enlarged pheromone-particle table, and (4) global best exchange. These hybrid systems were applied to data clustering. The experimental results utilizing public UCI datasets show that the performances of the proposed hybrid systems are superior compared to those of the K-mean, standalone PSO, and standalone ACOR. Among the four strategies of hybridization, the sequence approach with the enlarged pheromone table is superior to the other approaches because the enlarged pheromone table diversifies the generation of new solutions of ACOR and PSO, which prevents traps into the local optimum.

Ant Colony based Mobile Ad Hoc Networks Routing Protocols: A Review

obile ad-hoc networks (MANETs) are the wireless ad-hoc networks which enclose sovereign group of wireless mobile nodes. The communication is relayed via routes which are discovered using various routing protocols. Diverse routing protocols have been proposed so far in MANETs. Likewise, swarm intelligence (SI) based ant colony optimization (ACO) technique have various routing protocols which can also solve the routing problems in MANETs. ACO uses the chemical substance called Pheromone whose value is stored in pheromone table to find shortest path between source and destination. However our exigent task is to select optimal routing protocol for changeable network scenarios. This paper stated the functioning of existing ACO based ad-hoc routing protocols as well as ad-hoc routing protocols for MANETs and the comparison tables with summery of every protocol is shown. Moreover this review will help researchers in having glimpse of the existing protocols and thus can select optimal routing protocol which responds quickly on change of network topology.

AntP2PR: An ant intelligence inspired routing scheme for Peer-to-Peer networks

Unstructured Peer-to-Peer networks consist of an infrastructure-less overlay on top of another network. Most of them use distributed algorithms for all operations, such as resource discovery or connectivity control. Research has shown that a considerable amount of the generated traffic is due to signaling messages. Furthermore, another challenge when implementing a Peer-to-Peer network is avoiding free riders, i.e. users trying to profit from the network without sharing their resources. In this paper a new approach to routing packets in such networks is presented using ant intelligence. Success messages are used as agents and the biological procedure of pheromone trails is used for forwarding new packets used in resource discovery. These agents carry an amount of pheromone which will be added to a pheromone table representing routes to other peers. This approach enables the network to adjust to the dynamic nature of Peer-to-Peer networks where new nodes connect and disconnect continuously. Peers that are free riding will be ultimately isolated from the rest of the network by limiting the number of messages directed to them. The authors have simulated an unstructured Peer-to-Peer network, such as Gnutella, that uses this method and the results are very promising. The amount of traffic used solely for resource discovery is greatly reduced enabling the users to use more bandwidth for transferring content.

An Ant-Based On-Demand Energy Route Protocol for IEEE 802.15.4 Mesh Network

In this paper, an improved ant-based on-demand energy route (AOER) protocol for IEEE 802.15.4 mesh networks is presented. The information of network status is obtained by Forward_Ant and stored in an inversed pheromone table which simplifies the data structures of route packets. The simplified structure can avoid wasting of storage memories and processing capabilities which are very important for low-power wireless personal area network devices. The average path energy, average network energy and minimum remaining node energy of the path are combined to evaluate paths and update the inversed pheromone table. The positive and negative reinforcement of ant colony optimization algorithm are used to enhance the routes with better fitness. The pseudo-random-proportional-selection strategy is used to determine routes with the transferring of Backward_Ant from the destination to the source. Nodes with higher pheromone trail values are selected as the next hop with higher probabilities, therefore the network loads are distributed and nodes energy consumptions are balanced. A proactive route maintenance mechanism is also used in AOER to trigger route maintenance and avoid over-use of node energy. Experimental results on NS-2 show that AOER performs better than AODVjr in different network traffics and dynamics.

Dynamic ant programming for automatic construction of programs

AbstractA new method for automatic programming is proposed in this paper. Automatic programming is the method of generating computer programs automatically. Genetic programming (GP) is a typical example of automatic programming. GP evolves computer programs with tree structure based on genetic algorithm (GA). The new method is named dynamic ant programming (DAP). DAP is based on ant colony optimization (ACO) and uses dynamically changing pheromone table. The nodes (terminal and nonterminal) are selected using the value of pheromone table. The higher the rate of pheromone, the higher is the probability that it can be chosen. Although the search space (i.e., the pheromone table of DAP) is dynamically changing, the ants find good solution using portions of solutions, which are of pheromone value. We describe the method of construction of tree structure using ACO, as well as pheromone update and deletion and insertion of nodes in detail. DAP is applied to the symbolic regression problem that is widely used as a test problem for GP system. We compare the performance of DAP to GP and show the effectiveness of DAP. In order to investigate the influence of several parameters, we compare experimental results obtained using different settings. © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Ant-based survivable routing in dynamic WDM networks with shared backup paths

In this paper, we consider the problem of survivable routing in dynamic WDM networks with single link failure model. Our work mainly concerns in how to dynamically determine a protection cycle (i.e., two link-disjoint paths between a node pair) to establish a dependable lightpath with backup paths sharing. This problem is identified as NP-complete, thus a heuristic for finding near optimal solution with reasonable computation time is usually preferred. Inspired from the principle of ant colony optimization, we propose in this paper an ant-based mobile agents algorithm for this problem with improved blocking performance. To enable the new ant-based algorithm, we propose to use on each network node both a routing table that contains a set of feasible protection cycles between source destination nodes and also a pheromone table for mobile agents. By keeping a suitable number of mobile agents in a network to continually and proactively update the routing tables based on the current network congestion state, the routing solution of a connection request can be obtained with a reasonable computation time. Extensive simulation results upon the ns-2 network simulator and two typical network topologies show that our new algorithm can achieve a significantly lower blocking probability than the promising algorithm for dynamic lightpath protection proposed in [11] with a comparable computation complexity.

Ant-Based Alternate Routing in All-Optical WDM Networks

We propose an ant-based algorithm to improve the alternate routing scheme for dynamic Routing and Wavelength Assignment (RWA) in all-optical wavelength-division- multiplexing (WDM) networks. In our algorithm, we adopt a novel twin routing table structure that comprises both a P-route table for connection setup and a pheromone table for ants' foraging. The P-route table contains P alternate routes between a source-destination pair, which are dynamically updated by ant-based mobile agents based on current network congestion information. Extensive simulation results upon the ns-2 network simulator indicate that by keeping a suitable number of ants in a network to proactively and continually update the twin routing tables in the network, our new ant-based alternate routing algorithm can result in a small setup time and achieve a significantly lower blocking probability than the promising alternate shortest-path (ASP) algorithm and the fixed-paths least congestion (FPLC) algorithm for dynamic RWA even with a small value of P.

USING ANT AGENTS TO COMBINE REACTIVE AND PROACTIVE STRATEGIES FOR ROUTING IN MOBILE AD-HOC NETWORKS

This paper describes AntHocNet, an algorithm for routing in mobile ad-hoc networks based on ideas from the ant colony optimisation framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the source node uses ant agents to proactively search for alternatives and improvements of the original path. This allows to adapt to changes in the network, and to construct a mesh of alternative paths between source and destination. The proactive behaviour is supported by a lightweight information bootstrapping process. Paths are represented in the form of distance-vector routing tables called pheromone tables. An entry of a pheromone table contains the estimated goodness of going over a certain neighbour to reach a certain destination. Data are routed stochastically over the different paths of the mesh according to these goodness estimates. In an extensive set of simulation tests, we compare AntHocNet to AODV, a reactive algorithm which is an important reference in this research area. We show that AntHocNet can outperform AODV for different evaluation criteria in a wide range of different scenarios. AntHocNet is also shown to scale well with respect to the number of nodes.