Pheromone Trails Research Articles

An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time

In this article the scheduling problem of dynamic hybrid flow shop with uncertain processing time is investigated and an ant colony algorithm based rescheduling approach is proposed. In order to reduce the rescheduling frequency the concept of due date deviation is introduced, according to which a rolling horizon driven strategy is specially designed. Considering the importance of computational efficiency in the dynamic environment, the traditional ant colony optimization is improved. On the one hand, a strategy of available routes compression to restrict ants’ movement is proposed so that the ants’ searching cycle for new solutions could be shorten. On the other hand, illuminating function in state transfer possibility is improved to facilitate the exploration of low pheromone trail. Performance of rolling horizon procedure and rescheduling algorithm are evaluated respectively through simulations, the results show the best parameters of rolling horizon procedure and demonstrate the feasibility and efficiency of rescheduling algorithm. An example from the practical production is addressed to verify the effectiveness of the proposed approach.

Changes in the chemical profile of cephalic salivary glands of Scaptotrigona postica (Hymenoptera, Meliponini) workers are phase related.

Most advanced eusocial bees recruit their nest mates to food resources. Recent studies in Meliponini species have revealed that the cephalic salivary (labial) glands (CSGs) are responsible for the production of scent trail pheromones. Studies on CSGs have shown that changes occur in worker glandular cell morphology from emergence from brood combs until forager phase, which may be correlated to changes in the composition of the CSG secretion. However, the composition of the CSG secretion and the chemical changes that occur in it according to the worker's life phase or tasks performed are unknown for many species, including Scaptotrigona postica. In this study, the chemical profile of CSG secretion in S. postica workers was studied. Glands were taken from specimens that were newly emerged (NE), working in the brood comb area (CA) and foraging (FO), and were analyzed by gas chromatography-mass spectrometry. The results showed that the glandular secretion consists of oxygenated compounds of middle volatility (acids, alcohols, aldehydes, ketones, esters and ether), and their quantity varies among the different life phases, increasing as the individual moves from intra- to extra-colonial activities. The NE phase contained the smallest variety and quantity of compounds. Because of the variability of compounds, the CA workers were separated into three subgroups according to the chemical constitution of their secretion. Forager workers showed the largest quantity and variety of chemical compounds. The major compounds in forager gland secretion were 7-hexadecen-1-yl acetate and 5-tetradecen-1-yl acetate. Statistical analysis indicates that the chemical composition of glandular secretion is phase related.

Ants adjust their pheromone deposition to a changing environment and their probability of making errors.

Animals must contend with an ever-changing environment. Social animals, especially eusocial insects such as ants and bees, rely heavily on communication for their success. However, in a changing environment, communicated information can become rapidly outdated. This is a particular problem for pheromone trail using ants, as once deposited pheromones cannot be removed. Here, we study the response of ant foragers to an environmental change. Ants were trained to one feeder location, and the feeder was then moved to a different location. We found that ants responded to an environmental change by strongly upregulating pheromone deposition immediately after experiencing the change. This may help maintain the colony's foraging flexibility, and allow multiple food locations to be exploited simultaneously. Our treatment also caused uncertainty in the foragers, by making their memories less reliable. Ants which had made an error but eventually found the food source upregulated pheromone deposition when returning to the nest. Intriguingly, ants on their way towards the food source downregulated pheromone deposition if they were going to make an error. This may suggest that individual ants can measure the reliability of their own memories and respond appropriately.

Isolation and structure elucidation of a possible ant trail pheromone, from epothilone B producing fermentations of Sorangium cellulosum

Sorangium cellulosum is a myxobacteria producing epothilone B. During the development of the process to purify epothilone B from fermentation of Sorangium cellulosum we identified a possible ant trail pheromone. The initial detection, isolation and structure elucidation of S-3,4-dihydro-8-hydroxy-3,5,7-trimethylisocoumarin will be presented as well as literature regarding its biosignificance.

Composite collective decision-making.

Individual animals are adept at making decisions and have cognitive abilities, such as memory, which allow them to hone their decisions. Social animals can also share information. This allows social animals to make adaptive group-level decisions. Both individual and collective decision-making systems also have drawbacks and limitations, and while both are well studied, the interaction between them is still poorly understood. Here, we study how individual and collective decision-making interact during ant foraging. We first gathered empirical data on memory-based foraging persistence in the ant Lasius niger. We used these data to create an agent-based model where ants may use social information (trail pheromones), private information (memories) or both to make foraging decisions. The combined use of social and private information by individuals results in greater efficiency at the group level than when either information source was used alone. The modelled ants couple consensus decision-making, allowing them to quickly exploit high-quality food sources, and combined decision-making, allowing different individuals to specialize in exploiting different resource patches. Such a composite collective decision-making system reaps the benefits of both its constituent parts. Exploiting such insights into composite collective decision-making may lead to improved decision-making algorithms.

Desert ants use olfactory scenes for navigation

Desert ants, Cataglyphis fortis, forage for dead arthropods in the Tunisian salt pans. Both the unpredictable food distribution and the high surface temperatures might account for the fact that the ants do not use any pheromone trails. However, Cataglyphis has been shown to still use olfactory cues for navigation. For instance, the ants locate sparsely distributed food or pinpoint their inconspicuous nest entrance by following odour plumes. In this study we found that, as well as using odours to pinpoint a target, the ants might use environmental odours as olfactory landmarks when following habitual routes. When analysing odours collected at 100 positions in the desert, we found spatially distinct gradients of a range of different environmental odorants. Furthermore we confirm that individual foragers followed forager-specific routes when leaving the nest. Therefore these ants could potentially learn such olfactory landscape features along their stable routes. We, hence, asked whether ants could learn and use olfactory cues for route guidance. We trained ants to visit a stable feeder and presented them with a sequence of four different odours along the way. Homing ants that had already passed the odour alley on their way back were displaced to a remote test field and released at the starting point of an identical alley. Control ants that experienced the alley only during the test situation focused their search on the release point. Ants that had experienced the odours during training, however, biased their nest search towards the odour alley and performed straight walking segments along the alley. Hence, we found that ants learnt the olfactory cues along their homeward route and used these cues in the absence of other navigational information. Hence, desert ants seem to be able to use odour information to follow routes.

Exploration costs promote conservative collective foraging in the social caterpillar Malacosoma disstria

Collective choices of a food source are generally thought to arise from the amplification of individual preferences. However, costs associated with foraging can also influence food choice: animals faced with a poor-quality food source may either continue feeding on it (a conservative strategy), or search for a potentially more profitable one (a more risky strategy). Groups of nomadic, trail-following Malacosoma disstria caterpillars were offered a leaf of two different hosts, either side by side to determine preference, or at a distance to investigate how exploration costs affect food choice. Arenas with and without pheromone trails were used to distinguish between direct travel costs and unknown territory costs. When two leaves were side by side, groups of second-instar caterpillars showed clear preferences between host trees, whereas groups of fourth-instar larvae did not. When the two leaves were distant from each other, groups of second-instar caterpillars usually remained on the known source, irrespective of food preference or group size. These groups were more likely to relocate to a preferred food source when pheromone trails were present, suggesting that trails effectively decrease unknown territory costs. Groups of fourth-instar caterpillars were more likely to leave the known food source even when no trail was present, suggesting a developmental shift in costs associated with exploration. Our results show that collective choices reflect food preference when exploration costs are low (second-instar larvae with pheromone trails), but not when they are high (second-instar larvae without trails). Such a conservative foraging strategy can be adaptive if the exploration costs of leaving a known food source are greater than the potential benefits of discovering a better alternative, for example, in a homogenous environment like that of a tree.

The Hybrid ColorAnt-RT Algorithms and an Application to Register Allocation

Ant Colony Optimization is a metaheuristic used to create heuristic algorithms to find good solutions for combinatorial optimization problems. This metaheuristic is inspired on the effective behavior present in some species of ants of exploring the environment to find and transport food to the nest. Several works have proposed using Ant Colony Optimization algorithms to solve problems such as vehicle routing, frequency assignment, scheduling and graph coloring. The graph coloring problem essentially consists in finding a number k of colors to assign to the vertices of a graph, so that there are no two adjacent vertices with the same color. This paper presents the hybrid ColorAnt-RT algorithms, a class of algorithms for graph coloring problems which is based on the Ant Colony Optimization metaheuristic and uses Tabu Search as local search. The experiments with ColorAnt-RT algorithms indicate that changing the way to reinforce the pheromone trail results in better results. In fact, the results with ColorAnt-RT show that it is a promising option in finding good approximations of k. The good results obtained by ColorAnt-RT motivated it use on a register allocation based on Ant Colony Optimization, called CARTRA. As a result, this paper also presents CARTRA, an algorithm that extends a classic graph coloring register allocator to use the graph coloring algorithm ColorAnt-RT. CARTRA minimizes the amount of spills, thereby improving the quality of the generated code.

Collective decision making in a heterogeneous environment: Lasius niger colonies preferentially forage at easy to learn locations

Many ants forage in complex environments and use a combination of trail pheromone information and route memory to navigate between food sources and the nest. Previous research has shown that foraging routes differ in how easily they are learned. In particular, it is easier to learn feeding locations that are reached by repeating (e.g. left–left or right–right) than alternating choices (left–right or right–left) along a route with two T-bifurcations. This raises the hypothesis that the learnability of the feeding sites may influence overall colony foraging patterns. We studied this in the mass-recruiting ant Lasius niger. We used mazes with two T-bifurcations, and allowed colonies to exploit two equidistant food sources that differed in how easily their locations were learned. In experiment 1, learnability was manipulated by using repeating versus alternating routes from nest to feeder. In experiment 2, we added visual landmarks along the route to one food source. Our results suggest that colonies preferentially exploited the feeding site that was easier to learn. This was the case even if the more difficult to learn feeding site was discovered first. Furthermore, we show that these preferences were at least partly caused by lower error rates (experiment 1) and greater foraging speeds (experiment 2) of foragers visiting the more easily learned feeder locations. Our results indicate that the learnability of feeding sites is an important factor influencing collective foraging patterns of ant colonies under more natural conditions, given that in natural environments foragers often face multiple bifurcations on their way to food sources.

On the Amenability and Suitability of Ant Colony Algorithms for Convoy Movement Problem

Convoy Movement Problem (CMP) is a network optimization problem which consists of routing and scheduling military convoys between specific origin-and-destination pairs adhering to certain strategic constraints. This being an NP-hard problem intrinsically, the practical difficulty in obtaining exact solutions even for problems of modest size necessitates for quick solution procedures. As there is hardly any work related to the usage of Ant Colony Optimization (ACO) algorithms on CMP in the literature, this study examines the efficacy of ACO on CMP. The proposed version of ACO considers multiple ant colonies and introduces penalties while updating the trail as well as in the objective function whenever there is a violation of certain constraints. In this algorithm, a separate ant colony with unique pheromone deposits is assigned to each convoy and accordingly the trail of each colony is updated disregard to the pheromone trails of other colonies. The results obtained from this procedure are quite encouraging for a good number of problem instances. Sensitivity analysis is performed to assess the relationship between the number of ants (N) present in each colony, quality of the final solution and computational time. Lower the value of N, better the computational time but the quality of solution declines if N is chosen less than a certain threshold value due to insufficiency of ants.

An improved hybrid algorithm for the set covering problem

The state-of-the-art ant colony optimization (ACO) algorithm to solve large scale set covering problems (SCP) starts by solving the Lagrangian dual (LD) problem of the SCP to obtain quasi-optimal dual values. These values are then exploited by the ACO algorithm in the form of heuristic estimates. This article starts by discussing the complexity of this approach where a number of new parameters are introduced to escape local optimums and normalize the heuristic values. To avoid these complexities, we propose a new hybrid algorithm that starts by solving the linear programming (LP) relaxation of the SCP. This solution is used to eliminate unnecessary columns, and to estimate the heuristic information. To generate solutions, we use a Max–Min Ant System (MMAS) algorithm that employs a novel mechanism to update the pheromone trail limits to maintain a predetermined exploration rate. Computational experiments on different sets of benchmark instances prove that our proposed algorithm can be considered the new state-of-the-art meta-heuristic to solve the SCP.

Individual and Group Personalities Characterise Consensus Decision‐Making in an Ant

AbstractNon‐human animals can exhibit idiosyncratic behaviour across individuals in much in the same way as humans. Animals with specific personalities may have advantages in some environments, and this idiosyncrasy may thus be of considerable ecological and evolutionary importance. In group‐living organisms, personality can occur at the level of the group as well as that of the individual. However, at present, we have very little understanding of the possible benefits of group‐level personality, and how this is linked with individual personality. In this study, I examine the influence of individual and group personality during the process of colony migration in the Japanese ant, Myrmecina nipponica. These ants use a consensus decision process to decide among alternatives when searching for a new home. Individuals contribute to this process by scouting for new nest sites, recruiting nestmates by laying pheromone trails, and carrying brood to the new site, although whether these roles are consistent among individuals and how roles are distributed within and between colonies remain unclear. Individual contributions to the nest‐site selection process were quantified over five repeated relocations in five colonies. Results demonstrate that contributions to the relocation effort were highly skewed within the colonies and that individuals were consistent in their contributions over repeated relocation events. Furthermore, the distribution of effort differed between colonies, indicating that intercolony differences in composition of behavioural types resulted in colony‐level personality. While these differences did not lead to any detectable difference in relocation performance between colonies in the simple experimental arrangement used, colony personality could influence decision outcomes in more complex environments.

Trail following response of larval Cactoblastis cactorum to 2-acyl-1,3-cyclohexanediones.

Caterpillars of Cactoblastis cactorum secrete onto the surface of host cactuses droplets of an oily fluid that issues from the orifices of their paired mandibular glands. The fluid contains a series of 2-acyl-1,3-cyclohexanediones that, collectively, have been shown to elicit trail-following behavior from the caterpillars. This study reports the results of bioassays to determine the ability of two specific compounds, previously shown to be prominent components of the mandibular glands of pyralid caterpillars, 4-hydroxy-2-oleoyl-1,3-cyclohexanedione and 2-oleoyl-1,3-cyclohexanedione, to elicit trail-following behavior from the larvae of C. cactorum. Additionally bioassayed were structural fragments of these molecules. The relative effectiveness of the chemicals in eliciting trail following, the effect of varying concentration on the trail-following response, the importance of specific functional groups to the trail-following response, and the threshold sensitivity of the caterpillar to the pheromone were determined. The study showed that while all the tested compounds elicited some degree of trail following, they differed significantly in their effectiveness. The most effective of the compounds was 4-hydroxy-2-oleoyl-1,3-cyclohexanedione, which, on a per unit volume basis, was as effective as whole gland extract. Caterpillars secreted large quantities of fluid from the glands, and the threshold response to 4-hydroxy-2-oleoyl-1,3-cyclohexanedione occurred at a relative high application rate compared to trail pheromones of other social caterpillars and eusocial insects. This and the observation that the trail marker is secreted from the mandibular glands suggests that the use of 2-acyl-1,3-cyclohexanediones as trail markers is secondary, and that these compounds function primarily in some other, as yet undetermined, context.

A novel global Harmony Search method based on Ant Colony Optimisation algorithm

The Global-best Harmony Search (GHS) is a stochastic optimisation algorithm recently developed, which hybridises the Harmony Search (HS) method with the concept of swarm intelligence in the particle swarm optimisation (PSO) to enhance its performance. In this article, a new optimisation algorithm called GHSACO is developed by incorporating the GHS with the Ant Colony Optimisation algorithm (ACO). Our method introduces a novel improvisation process, which is different from that of the GHS in the following aspects. (i) A modified harmony memory (HM) representation and conception. (ii) The use of a global random switching mechanism to monitor the choice between the ACO and GHS. (iii) An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The proposed GHSACO algorithm has been applied to various benchmark functions and constrained optimisation problems. Simulation results demonstrate that it can find significantly better solutions when compared with the original HS and some of its variants.

Modeling shortest path selection of the ant Linepithema humile using psychophysical theory and realistic parameter values

The emergence of self-organizing behavior in ants has been modeled in various theoretical approaches in the past decades. One model explains experimental observations in which Argentine ants (Linepithema humile) selected the shorter of two alternative paths from their nest to a food source (shortest path experiments). This model serves as an important example for the emergence of collective behavior and self-organization in biological systems. In addition, it inspired the development of computer algorithms for optimization problems called ant colony optimization (ACO). In the model, a choice function describing how ants react to different pheromone concentrations is fundamental. However, the parameters of the choice function were not deduced experimentally but freely adapted so that the model fitted the observations of the shortest path experiments. Thus, important knowledge was lacking about crucial model assumptions. A recent study on the Argentine ant provided this information by measuring the response of the ants to varying pheromone concentrations. In said study, the above mentioned choice function was fitted to the experimental data and its parameters were deduced. In addition, a psychometric function was fitted to the data and its parameters deduced. Based on these findings, it is possible to test the shortest path model by applying realistic parameter values. Here we present the results of such tests using Monte Carlo simulations of shortest path experiments with Argentine ants. We compare the choice function and the psychometric function, both with parameter values deduced from the above-mentioned experiments. Our results show that by applying the psychometric function, the shortest path experiments can be explained satisfactorily by the model. The study represents the first example of how psychophysical theory can be used to understand and model collective foraging behavior of ants based on trail pheromones. These findings may be important for other models of pheromone guided ant behavior and might inspire improved ACO algorithms.

A novel global harmony search method based off-line tuning of RFNN for adaptive control of uncertain nonlinear systems

Purpose – The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller. Design/methodology/approach – The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure. Findings – First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem. Originality/value – The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

Flight in nature II: How animal flyers land

AbstractIn this review paper, different landing strategies of diverse species of animal flyers and gliders, both extinct and extant, are analysed. These methods vary depending on the animal group and the sensory system used by the animal to detect its landing site. In almost all species the use of delayed stall during the landing manoeuvre was observed. Sometimes wing flapping was used to aid in deceleration. With respect to guidance and navigation, most insect, bird and mammal gliders use their vision to guide them to landing via optical flow or motion parallax. Bats, which are nocturnal creatures, rely on their auditory system as they use echolocation to find their nesting site. Some butterfly and moth species guide themselves to landing using their olfactory sense as they follow pheromone trails. The information presented here can be used as a source of information for novel bio-inspired unmanned aircraft design.

Chemistry of Trail Pheromones from Cubitermes Termites (Amitermes dentatus): An Innovation in Pest Management

Chemistry of Trail Pheromones from Cubitermes Termites (Amitermes dentatus): An Innovation in Pest Management

A Routing Filter for the Real-time Railway Traffic Management Problem Based on Ant Colony Optimization

The growth in transport demand of the last years is a trend expected to continue in the coming years. In railways, the difficulties in building new infrastructures due to high costs or physical obstacles have translated into the need to utilize the already existing infrastructures at their full capacity. During the daily operations disturbances may happen, creating overlapping requests for the same tracks at the same time by multiple trains. Traffic controllers are thus required to solve these problems, taking decisions on how to answer the overlapping requests. This may lead to the creation of consecutive delays on the network, which in turn may create new conflicting requests and so on, impoverishing the quality of the service offered. In order to minimize the propagation of consecutive delays, thus recovering the quality of the service, real-time traffic adjustments are necessary. A stream of research focuses on the real-time railway traffic management problem. In this problem both routing and scheduling decisions are considered simultaneously. The problem dimension and the computational time required to find a solution of acceptable quality are strongly affected by the characteristics of the rail network and traffic flows, and in particular by the number of routing combinations that can be assigned to the trains. This paper presents a new filtering method for the selection of a set of possible routings for each train, whose maximum dimension is a parameter to be set, in order to facilitate the computation of a good solution for the subsequent train routing and scheduling problem. We call this the routing selection problem. It represents a first step in the solution process of the real-time railway traffic management problem. We model this problem using a N-partite graph in which each partition represents the set of alternative routings for a train. To solve the routing selection problem we use ant colony optimization, a meta-heuristic based on ant colonies’ behaviour. Each ant builds a solution by assigning one routing to each train based on heuristic information and pheromone trails. The heuristic information is a greedy measure of the effect of a particular assignment on the overall solution quality. The pheromone trail represents the shared knowledge on the quality of the previously built solutions which include the assignment itself. A pool of good quality solutions are generated and the corresponding combination of routings is released as input to the real-time railway traffic management solver.

An Introductory Course in the Computational Modeling of Nature

Abstract This introductory course in computational modeling of nature contains the development of three kinds of models of phenomena in nature -- agent-based models and simple finite difference models using the environment of the NetLogo language and complex finite difference models using the language of C + +. No prior programming experience is assumed. The natural phenomena modeled include some standard ones (e.g. ants following pheromone trails, the interaction of sheep and wolves) and some non-standard ones (the creation of the world, 3 dogs playing games, and formation of stripes and spots in the skins of animals). The emphasis of the course is on the modeling process. A distinguishing feature is that students are able to compare and critique these models.