Showing papers on "Ant colony published in 2016"
TL;DR: A hybrid metaheuristic algorithm based on an ant colony system (ACS) and a variable neighborhood search (VNS) is developed for its solution which is a popular extension of the basic Vehicle Routing Problem arising in real world applications.
Abstract: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is considered.A hybrid algorithm that combines ACS and VNS approach is developed.A number of well-known benchmark problems are solved and compared.Proposed algorithm outperformed perturbation based VNS and local search based ACS.Some new best solutions for benchmark problem instances are found. Along with the progress in computer hardware architecture and computational power, in order to overcome technological bottlenecks, software applications that make use of expert and intelligent systems must race against time where nanoseconds matter in the long-awaited future. This is possible with the integration of excellent solvers to software engineering methodologies that provide optimization-based decision support for planning. Since the logistics market is growing rapidly, the optimization of routing systems is of primary concern that motivates the use of vehicle routing problem (VRP) solvers as software components integrated as an optimization engine. A critical success factor of routing optimization is quality vs. response time performance. Less time-consuming and more efficient automated processes can be achieved by employing stronger solution algorithms. This study aims to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) which is a popular extension of the basic Vehicle Routing Problem arising in real world applications where pickup and delivery operations are simultaneously taken into account to satisfy the vehicle capacity constraint with the objective of total travelled distance minimization. Since the problem is known to be NP-hard, a hybrid metaheuristic algorithm based on an ant colony system (ACS) and a variable neighborhood search (VNS) is developed for its solution. VNS is a powerful optimization algorithm that provides intensive local search. However, it lacks a memory structure. This weakness can be minimized by utilizing long term memory structure of ACS and hence the overall performance of the algorithm can be boosted. In the proposed algorithm, instead of ants, VNS releases pheromones on the edges while ants provide a perturbation mechanism for the integrated algorithm using the pheromone information in order to explore search space further and jump from local optima. The performance of the proposed ACS empowered VNS algorithm is studied on well-known benchmarks test problems taken from the open literature of VRPSPD for comparison purposes. Numerical results confirm that the developed approach is robust and very efficient in terms of both solution quality and CPU time since better results provided in a shorter time on benchmark data sets is a good performance indicator.
125 citations
TL;DR: The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy.
114 citations
TL;DR: A model entirely based on experimental data confirms that the individual level interactions and building rules are sufficient to reproduce the nest growth dynamics and the spatial patterns observed for real ant nests, and suggests that the lifetime of the pheromone is a highly influential parameter that controls the growth and form of nest architecture.
Abstract: The nests of social insects are not only impressive because of their sheer complexity but also because they are built from individuals whose work is not centrally coordinated. A key question is how groups of insects coordinate their building actions. Here, we use a combination of experimental and modeling approaches to investigate nest construction in the ant Lasius niger. We quantify the construction dynamics and the 3D structures built by ants. Then, we characterize individual behaviors and the interactions of ants with the structures they build. We show that two main interactions are involved in the coordination of building actions: (i) a stigmergic-based interaction that controls the amplification of depositions at some locations and is attributable to a pheromone added by ants to the building material; and (ii) a template-based interaction in which ants use their body size as a cue to control the height at which they start to build a roof from existing pillars. We then develop a 3D stochastic model based on these individual behaviors to analyze the effect of pheromone presence and strength on construction dynamics. We show that the model can quantitatively reproduce key features of construction dynamics, including a large-scale pattern of regularly spaced pillars, the formation and merging of caps over the pillars, and the remodeling of built structures. Finally, our model suggests that the lifetime of the pheromone is a highly influential parameter that controls the growth and form of nest architecture.
88 citations
TL;DR: The results of the multiple simulations were able to show that LTAWSN, in comparison with the previous ant colony based routing algorithm, energy aware ant colony routing algorithms for the routing of wireless sensor networks, ant colony optimization-based location-aware routing algorithm for wireless Sensor networks and traditional ant colony algorithm, increase the efficiency of the system, obtains more balanced transmission among the nodes and reduce the energy consumption of the routing and extends the network lifetime.
Abstract: Reducing the energy consumption of network nodes is one of the most important problems for routing in wireless sensor networks because of the battery limitation in each sensor. This paper presents a new ant colony optimization based routing algorithm that uses special parameters in its competency function for reducing energy consumption of network nodes. In this new proposed algorithm called life time aware routing algorithm for wireless sensor networks (LTAWSN), a new pheromone update operator was designed to integrate energy consumption and hops into routing choice. Finally, with the results of the multiple simulations we were able to show that LTAWSN, in comparison with the previous ant colony based routing algorithm, energy aware ant colony routing algorithms for the routing of wireless sensor networks, ant colony optimization-based location-aware routing algorithm for wireless sensor networks and traditional ant colony algorithm, increase the efficiency of the system, obtains more balanced transmission among the nodes and reduce the energy consumption of the routing and extends the network lifetime.
88 citations
TL;DR: Though Cretaceous stem-group ants were eusocial and adaptively diverse, it is hypothesize that their extinction resulted from the rise of competitively superior crown-group taxa that today form massive colonies, consistent with Wilson and Hölldobler's concept of "dynastic succession."
83 citations
07 Jun 2016
TL;DR: It is aimed to implement an obstacle avoidance UAV path planning by using Multi-Colony ACO algorithm, in which a number of ant colonies try to find an optimal solution cooperatively by exchanging their valuable information with each other.
Abstract: In recent years, the availability of low-cost and autonomous unmanned aerial vehicles (UAVs) results in the use of them for different types of military and commercial applications. The crucial part of the autonomous UAVs is their online or offline path planning algorithms. In the literature, there are many types of solutions, which use evolutionary and/or swarm intelligence approaches. Ant colony optimization is one of the mostly used algorithms, which has been applied to solve different type of path planning problems. Mainly, most of these studies have focused on a single colony ant colony optimization (ACO), which can find better solutions in fewer computation times. However, it is able to converge to a sub-optimal solution in the planning process. One approach to avoid the premature convergence is the use of Multi-Colony ACO, in which a number of ant colonies try to find an optimal solution cooperatively by exchanging their valuable information with each other. In this paper, it is aimed to implement an obstacle avoidance UAV path planning by using Multi-Colony ACO algorithm. We experimentally investigate the use of Multi-Colony ACO approach results from an effective path planning for UAVs with a comparison to a single colony ACO approach.
65 citations
TL;DR: An algorithm using an improved fuzzy ant colony system (ACS) is proposed in order to solve the routing problem usually faced by on-site service companies and shows that the proposed algorithm performs better than the previous fuzzy-ACS algorithm without cluster insertion algorithm.
62 citations
01 May 2016
TL;DR: A bio-inspired meta-heuristic and mathematically probabilistic technique of the Ant Colony Optimization (ACO) where efficient path establishment and information transfer can be achieved and this project tries to become a state of art technology for the benefit of society and the country.
Abstract: The Vehicular Ad Hoc Networks (VANET), which are essentially the subset of Mobile Ad Hoc Networks (MANET) have been focused in the recent years mainly for the research and development of the Intelligent Transport Systems having the ability for both self-management and also self-organization, making them reliable as a highly mobile network system Also, the disconnection of such high mobile nodes will be a problem in VANET structure, where the loss of information will be critical because the vehicles/nodes in a VANET can move at a speed of 300 km/h or 186.41 miles/h. The protocols suggested earlier used a fixed topology for the mobile nodes in VANET. Even though the scientists had proposed different algorithms like beaconing, greedy or moving directional approach, the environmental changes were ignored which usually play an important criterion in regulation of information. In this paper, we propose a bio-inspired meta-heuristic and mathematically probabilistic technique of the Ant Colony Optimization (ACO) where efficient path establishment and information transfer can be achieved. Path availability and the delay time have been used for the evaluation of discovered paths. But, here the real time environmental changes were taken into account and the performance was measured in accordance with ACO. The technical software for VANET implementation using modifications in the ACO was implemented in the Matrix Laboratory (MATLAB)-2015b simulator along with the different randomized changes in environmental conditions. The random movements of the ants have displayed an efficient means for the delivery of packets to the maximum number of available nodes/vehicles in the network with very low latency. So that even if accidental failure of any node occurs, the surrounding ant neighbors will carry the required information to the desired nodes resulting in improvement of the throughput. Thus, the results obtained through various environmental modifications indicated that the use of randomized ACO algorithm for a highly mobile VANET system offers a much higher performance as compared to other earlier suggested on demand methods and can be realized commercially. Hence, this project tries to become a state of art technology for the benefit of society and the country.
58 citations
TL;DR: In this article, a hybrid agent-based ant colony optimization-genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem, which provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain.
Abstract: Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions.
53 citations
TL;DR: It is argued that this approach will provide new and complementary insights into the evolutionary and ecological dynamics between ants and their many associates, and will facilitate comparisons across different ant-symbiont assemblages as well as across different types of ecological networks.
Abstract: Ant colonies provide well-protected and resource-rich environments for a plethora of symbionts. Historically, most studies of ants and their symbionts have had a narrow taxonomic scope, often focusing on a single ant or symbiont species. Here we discuss the prospects of studying these assemblies in a community ecology context using the framework of ecological network analysis. We introduce three basic network metrics that we consider particularly relevant for improving our knowledge of ant-symbiont communities: interaction specificity, network modularity, and phylogenetic signal. We then discuss army ant symbionts as examples of large and primarily parasitic communities, and symbiotic sternorrhynchans as examples of generally smaller and primarily mutualistic communities in the context of these network analyses. We argue that this approach will provide new and complementary insights into the evolutionary and ecological dynamics between ants and their many associates, and will facilitate comparisons across different ant-symbiont assemblages as well as across different types of ecological networks.
51 citations
TL;DR: A trap-jaw ant from 99 million-year-old Burmese amber is described with head structures that presumably functioned as a highly specialized trap for large-bodied prey and scythe-like mandibles that extend high above the head, both demonstrating the presence of exaggerated morphogenesis early among stem-group ants.
TL;DR: This article presents three novel parallel versions of the Ant Colony System (ACS) for the graphics processing units (GPUs) which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task.
TL;DR: This work describes a parallelization strategy that leverages the inherently stochastic and distributed nature of Ant colony optimisation to develop metaheuristic-based solutions to “real world” problems on high-performance, energy-efficient contemporary heterogeneous computing platforms.
Abstract: Ant colony optimisation (ACO) is a nature-inspired, population-based metaheuristic that has been used to solve a wide variety of computationally hard problems. In order to take full advantage of the inherently stochastic and distributed nature of the method, we describe a parallelization strategy that leverages these features on heterogeneous and large-scale, massively-parallel hardware systems. Our approach balances workload effectively, by dynamically assigning jobs to heterogeneous resources which then run ACO implementations using different search strategies. Our experimental results confirm that we can obtain significant improvements in terms of both solution quality and energy expenditure, thus opening up new possibilities for the development of metaheuristic-based solutions to "real world" problems on high-performance, energy-efficient contemporary heterogeneous computing platforms.
01 Oct 2016
TL;DR: The results show that a heterogeneous fleet is preferred to a homogenous fleet as it generates more cost savings under variable customer demands.
Abstract: Display Omitted Vehicle routing with fleet heterogeneity, mixed delivery and pickup, and time windows.Proposing a two-stage, meta-heuristic ant colony system algorithm.Jointly optimizing the vehicle type, the vehicle number, and the travel routes.A heterogeneous fleet may result in larger cost savings than a homogenous fleet. Vehicle heterogeneity and backhaul mixed-load problems are often studied separately in existing literature. This paper aims to solve a type of vehicle routing problem by simultaneously considering fleet heterogeneity, backhaul mixed-loads, and time windows. The goal is to determine the vehicle types, the fleet size, and the travel routes such that the total service cost is minimized. We propose a multi-attribute Label-based Ant Colony System (LACS) algorithm to tackle this complex optimization problem. The multi-attribute labeling technique enables us to characterize the customer demand, the vehicle states, and the route options. The features of the ant colony system include swarm intelligence and searching robustness. A variety of benchmark instances are used to demonstrate the computational advantage and the global optimality of the LACS algorithm. We also implemented the proposed algorithm in a real-world environment by solving an 84-node postal shuttle service problem for China Post Office in Guangzhou. The results show that a heterogeneous fleet is preferred to a homogenous fleet as it generates more cost savings under variable customer demands.
TL;DR: Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the clustersupply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.
Abstract: Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant’s spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony’s social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.
TL;DR: The results showed that the proposed ACS-IPTBK scheme has a slightly slower convergence speed than does the conventional ACS algorithm, but yields a more globally optimal solution for the attack path, particularly in large-scale network topologies.
TL;DR: It is confirmed that it is reasonable to use the pheromone trail and balanced heuristics in the construction of decision trees and found that, for the ACDT algorithm, good results can also be obtained without heuristic.
TL;DR: The abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering and produces results that are not only more accurate but also more efficiently determined than the ant Colony Clustering algorithm and the other methods.
Abstract: Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.
TL;DR: An algorithm to choose optimal path for data delivery for continuous monitoring of vital signs of patients in Body Area Network in hospital indoor environments where a large number of patients exist and the traffic generated on the path rapidly changes over time is presented.
TL;DR: This study poses nutritional challenges to trap-jaw ants, Odontomachus hastatus, and explores their response in terms of survival, foraging behaviour and energy storage, and shows that ants had an extraordinary capacity to regulate the amounts of food entering the nest.
TL;DR: The numerical investigation provides computational proof of the utility of the daemon actions and infers that these latter can be applied either to the initial or to subsequent colonies and shows that ASV obtains the optimum for most small-sized instances.
Abstract: Single-machine weighted earliness tardiness scheduling is a prevalent problem in just-in-time production environments. Yet, the case with distinct due dates is strongly NP-hard. Herein, it is approximately solved using ASV, an ant colony-based system with a reduced number of ants and of colonies and with daemon actions that explore the search space around the ants using a variable neighborhood search (VNS). The numerical investigation provides computational proof of the utility of the daemon actions. In addition, it infers that these latter can be applied either to the initial or to subsequent colonies. Furthermore, it highlights the importance of using ant colony optimization as the multiple restart engine of VNS. Finally, it shows that ASV obtains the optimum for most small-sized instances. It has a 0.2 % average deviation from the optimum over all benchmark instances.
TL;DR: In this article, an open shop scheduling problem based on a mechanical production workshop to minimize the total flow time including a multi-skill resource constraint is considered, where a number of workers have a versatility to carry out different tasks and according to their assignment a schedule is generated.
Abstract: The continuous evolution of manufacturing environments leads to a more efficient production process that controls an increasing number of parameters. Production resources usually represent an important constraint in a manufacturing activity, specially talking about the management of human resources and their skills. In order to study the impact of this subject, this paper considers an open shop scheduling problem based on a mechanical production workshop to minimise the total flow time including a multi-skill resource constraint. Then, we count with a number of workers that have a versatility to carry out different tasks, and according to their assignment a schedule is generated. In that way, we have formulated the problem as a linear as and a non-linear mathematical model which applies the classic scheduling constraints, adding some different resources constraints related to personnel staff competences and their availability to execute one task. In addition, we introduce a genetic algorithm and an ant co...
TL;DR: It is found that private information is sufficient to trigger trapping in selecting the poorer of two food sources, and may be sufficient to cause it altogether, even when pheromone trails are removed.
Abstract: Ant colonies are famous for using trail pheromones to make collective decisions. Trail pheromone systems are characterised by positive feedback, which results in rapid collective decision making. However, in an iconic experiment, ants were shown to become 'trapped' in exploiting a poor food source, if it was discovered earlier. This has conventionally been explained by the established pheromone trail becoming too strong for new trails to compete. However, many social insects have a well-developed memory, and private information often overrules conflicting social information. Thus, route memory could also explain this collective 'trapping' effect. Here, we disentangled the effects of social and private information in two 'trapping' experiments: one in which ants were presented with a good and a poor food source, and one in which ants were presented with a long and a short path to the same food source. We found that private information is sufficient to trigger trapping in selecting the poorer of two food sources, and may be sufficient to cause it altogether. Memories did not trigger trapping in the shortest path experiment, probably because sufficiently detailed memories did not form. The fact that collective decisions can be triggered by private information alone may require other collective patterns previously attributed solely to social information use to be reconsidered.
TL;DR: Simulation results showed that, the free step length ant colony algorithm could find a shorter path and its convergence was better compared with the traditional ant colony algorithms.
Abstract: An improved ant colony algorithm was proposed with the unlimited step length of finding optimal path. It aims at the shortcomings of the traditional ant colony algorithms such as the single step le...
TL;DR: An improved ant colony optimization is developed for pipe routing, which includes the classification of ant colony, rank mechanism updating rule of pheromone intensity, and a dynamic updatingRule of heuristic information.
Abstract: Aircraft engines usually contain a lot of pipes and cables whose routing design greatly affects engine performance and reliability. In this paper a pipe routing approach for aircraft engines based on ant colony optimization is proposed, which contains improvements in three aspects. First, constraints over aircraft engine pipe routing are formulated and classified with respect to nine aspects. Second, a fan annual mesh of the layout space is developed, and two-mesh division is conducted to store and update environmental information about the layout space. Every unit obtained by the layout space modeling has been assigned a potential value to meet constraints and represent different areas in the layout space. Third, an improved ant colony optimization is developed for pipe routing, which includes the classification of ant colony, rank mechanism updating rule of pheromone intensity, and a dynamic updating rule of heuristic information. Every ant can search for 26 adjacent units in random directions a...
24 Aug 2016
TL;DR: A new model of Ant Colony Optimization using multiple colonies with different level of sensitivity to the ant’s pheromone is introduced, which allows the exploration of the solution space to be extended.
Abstract: In order to solve combinatorial optimization problem are used mainly hybrid heuristics. Inspired from nature, both genetic and ant colony algorithms could be used in a hybrid model by using their benefits. The paper introduces a new model of Ant Colony Optimization using multiple colonies with different level of sensitivity to the ant’s pheromone. The colonies react different to the changing environment, based on their level of sensitivity and thus the exploration of the solution space is extended. Several discussion follows about the fuzziness degree of sensitivity and its influence on the solution of a complex problem.
TL;DR: Topics discussed include experiments on ant colony's behavior, role of evolution in shaping the collective behavior in ants, and a study which aim to understand the algorithm use by ants in maintaining and repairing their trails.
Abstract: The article offers the author's insights on the social behavior of ants. Topics discussed include experiments on ant colony's behavior, role of evolution in shaping the collective behavior in ants, and a study which aim to understand the algorithm use by ants in maintaining and repairing their trails.
01 Dec 2016
TL;DR: In this paper, an ant colony algorithm for proportional plus integral (PI) optimal tuning to design load frequency controllers is introduced, and a mathematical model of a network with DFIG based wind turbine in two area frequency control is developed.
Abstract: In this research, ant colony algorithm for proportional plus integral (PI) optimal tuning to design load frequency controllers is introduced. A mathematical model of a network with DFIG based wind turbine in two area frequency control is developed. Time domain simulations of the various interconnected power system areas subjected to major disturbances are investigated. A method to enhance the system frequency performance based on ant colony optimization for various wind energy penetration scenarios is presented. Simulated results show that the ant colony controller has a significant capability to enhance the frequency performance.
TL;DR: This article presents an original solution of authors to the MDVRP problem via ACO algorithm including its principles and parameters and several examples and experiments are shown.
Abstract: The multi-depot vehicle routing problem MDVRP is an extension of a classic vehicle routing problem VRP. There are many heuristic and metaheuristic algorithms e.g., tabu search, simulated annealing, genetic algorithms as this is an NP-hard problem and, therefore, exact methods are not feasible for more complex problems. Another possibility is to adapt the ant colony optimisation ACO algorithm to this problem. This article presents an original solution of authors to the MDVRP problem via ACO algorithm. The first part deals with the algorithm including its principles and parameters. Then several examples and experiments are shown.
TL;DR: It is found that slavemakers drive recognition cue diversity in their ant hosts, in much the same way that avian hosts diversify their egg appearance in response to brood parasite pressure, and such recognition cue diversification through negative frequency-dependent selection favors rare host phenotypes.
Abstract: Social insect colonies defend themselves from intruders through nestmate recognition, yet the evolution and maintenance of recognition cue diversity is still poorly understood. We compared the recognition cue diversity of 9 populations of Temnothorax longispinosus ant colonies, including populations that harbored the socially parasitic slavemaker ant, Protomognathus americanus. Although ants recognize friends from foe based on recognition cues encoded in their cuticular hydrocarbon profile, which specific compounds are involved in recognition is unknown for most species. We therefore started by statistically identifying 9 putative recognition compounds involved in worker and colony aggression. We find that colonies that co-occur with slavemakers were more variable in these recognition compounds and hence less similar in their recognition profiles than unparasitized populations. Importantly, these differences appear to be regulated by processes that specifically act on the level of the colony, which rules out potentially confounding effects altering chemical profiles of populations, such as differences in abiotic conditions or standing genetic variation. Instead, our findings indicate that slavemakers drive recognition cue diversity in their ant hosts, in much the same way that avian hosts diversify their egg appearance in response to brood parasite pressure. Such recognition cue diversification through negative frequency-dependent selection favors rare host phenotypes and renders it impossible for parasites to match the recognition profile of all potential hosts.