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The optimization of the cutting path of the sample can effectively reduce the cutting time, thereby improving the production efficiency of numerical control processing. This paper comprehensively considers the impact of the cutting order and the position of the knife entry point on the cutting path, converts the cutting path problem into a type of traveling salesman problem (TSP), and proposes an improved genetic-particle swarm optimization algorithm. The selection mechanism of the algorithm combines the elitist retention strategy and roulette wheel selection method to accelerate the search for the optimal solution; the mutation strategy designs a linear decreasing mutation rate, which enhances the global search ability; at the same time, introduces the ant colony optimization algorithm to process the fitness function, adjusts the population evolution difference, and speeds up the optimization process. Through this hybrid algorithm, the cutting order of the sample can be quickly optimized, and the nearest neighbor algorithm is used to determine the position of the knife entry point. Tests are conducted on clothing patterning charts and standard examples. Compared with several commonly used algorithms, experimental results verify the feasibility and effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

Although wind is a ubiquitous component of ecological systems and might affect pheromone communication, its effects have not been studied in depth. To test whether wind impacts trail pheromone behaviour, we studied the behaviour of the leaf-cutting ant Acromyrmex lobicornis in natural nests. We conducted observations under (1) unaltered scenarios with different wind intensities and (2) manipulative experiments aimed at altering trail-marking pheromone volatilization rates. In our manipulative experiments, we placed filter paper on ant trails, then removed the pheromone-marked paper 24 h later and either exposed or did not expose the marked paper to artificial wind (2 km/h) for 1 or 3 h. We then put the marked paper back on the trail and filmed ants' responses for 1 min. As controls, we filmed ants' responses to filter paper immediately before it was removed as well as their responses to new unmarked filter paper. We measured ant flux, speed and walking sinuosity of ants over trails and filter paper, the number of ants tapping their gaster against the substrate (an indication of pheromone-marking frequency), the number of head-on encounters among workers (as alternative modes of communication) and ant body size. Wind negatively affected ant traffic by decreasing ant flux and speed. Colonies increased the number of U-turns and head-on encounters among workers, with a higher response as wind exposure time increased, but trail pheromone deposition was constant among treatments. The size frequency distribution of foragers over the treatment area was skewed towards larger ants on trails with unmarked paper and on trails with paper that had been exposed to wind for 1 or 3 h or not exposed to wind but removed for 3 h. This could be the result of small ants focusing on pheromone trail maintenance, while larger ones focused on foraging on the filter paper irrespective of whether it had been marked. Our results suggest that division of labour and behavioural plasticity might allow leaf-cutting ants to mitigate the effects of wind on pheromone communication and continue foraging. • Wind negatively affected ant traffic on trails by decreasing ant flux and speed. • Ants performed more U-turns and head-on encounters on trails exposed to wind. • Wind exposure did not affect trail pheromone deposition. • Wind exposure influenced the size frequency distribution of forager ants. [ABSTRACT FROM AUTHOR]

The geogr aphy of paper is par t icul ar l y wel l capt ur ed in a chapt er on Al br echt Dür er (1471-1528), in which Fowl er discusses Dür er 's pr efer ence for bl ue paper dur ing his visit t o Venice and in his commissions for t he Venet o r egion (1505-07). Fr om t heir int r oduct ion in t he fift eent h cent ur y, pr int ing pr esses wer e t he cheapest, most effect ive way of pr oducing a l ar ge number of copies of t he same document and wer e onl y oust ed - in t er ms of simpl icit y and cost - by t he invent ion of cycl ost yl e st encil dupl icat or s in t he 1880s. In t he absence of copyr ight, it was al most impossibl e t o make a l iving as a wr it er, since pr ofit s accr ued most l y t o t he publ isher s, who al l t oo fr equent l y suffer ed fr om pir at e r epr int s, whil e t he best r ewar d an aut hor coul d hope for was a gener ous pr esent fr om a dedicat ee. Al beit bur densome, al so due t o t he accompanying par apher nal ia of l ead t ype, which was never l ight nor easy t o shift, t heir ut il it y meant t hat pr esses wer e oft en on t he move, which for ms t he subject of t he pr esent book (t he al t er nat ive, mor e evocat ive, t it l e - La stampa è mobile - unfor t unat el y had al r eady been t aken (p. 31)). [Extracted from the article]

Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage. [ABSTRACT FROM AUTHOR]

The conflict between climate change and economic growth is one of the most significant dilemmas of our era. Under the prevalent discourse of sustainable development and green economy, many believe that market-based approaches can address environmental problems under the current capitalist economic regime. This article aims to analyse Ant Forest, a gamified green initiative launched by the biggest fintech company in China, to combat climate change through cultivating users' green lifestyles. Based on semi-structured interviews with the game-users, the paper argues that there is an inconsistency between the environmental value of Ant Forest and the real impact it has on the participants' consumption behaviour. As a capitalist green initiative based on consumption and market forces, Ant Forest reflects the basic logic of competition and expansion. In addition, it enhances the users' reliance on its related business entities and further alienates the users from the rest of nature by creating a knowledge gap that impedes its users from fully knowing the environmental implications of their consumption behaviour. [ABSTRACT FROM AUTHOR]

Copyright of Revista Biociências is the property of Revista Biociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problems of slow convergence and insufficient accuracy of traditional ant colony algorithms in solving large-scale problems, this paper proposes a multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model (GHMACO). Firstly, the heterogeneous multi-ant colony model is constructed, and each colony collaborates to improve the performance of the algorithm. Secondly, in order to enhance the communication among the heterogeneous colonies, a non-cooperative game strategy is introduced. The heterogeneous ant colonies are divided into the propagating colony and the absorbing colonies, where the propagating colony propagates the optimal payoffs of the game, and the absorbing colonies choose optimal strategies for absorption to balance the convergence and diversity of the algorithm. Further, the hierarchical temporal memory model is adopted to perform hierarchical optimization strategies based on path memories which includes: local exploration strategy, pheromone redistribution strategy and path replacement strategy, thus improving the accuracy of the algorithm and helping the colonies to jump out of the local optimum. Experiments on the traveling salesman problem show that the improved algorithm is effective in improving the convergence and accuracy of the traditional ant colony algorithms, especially in large-scale problems. [ABSTRACT FROM AUTHOR]

Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. [ABSTRACT FROM AUTHOR]

Several algorithms have been proposed in recent decades to solve optimization problems, some of which have been inspired by nature. The collective behavior of ants is an example of intelligence in the nature which has been a source of inspiration for optimization algorithms. Based on the concept of path integration, ants are able to continuously calculate their current location from their previous trajectory when exploring to find food. In the return journey, they go by calculating the outcome of the route, without returning exactly from their path, choosing a direct path to return to their original point. In this paper, the behavior of desert ants in finding their return path to the nest is the source of inspiration. By modeling the swarm intelligence of these ants based on the results of empirical scientific research, a new optimization algorithm is presented, termed Ant Path Integration. In this paper, the mathematical relationships governing the concept of path integration are presented. Simulation results on a variety of benchmark functions, including some of the CEC2019 benchmarks, show the superiority of this algorithm in finding the optimal solution in comparison to some well-known optimization algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slow convergence and low accuracy of the traditional ant colony algorithm in solving the traveling salesman problem (TSP), this paper proposes a heterogeneous ant colony algorithm based on the selective evolution mechanism and game strategy (SGHACA). First, we propose a game mechanism. In each iteration, the best population and the worst population will play a game. At the end of the game, pheromone gains will be given to the participants according to the weight coefficients to motivate the populations to participate in the next round of the game, in order to promote cooperation among different populations and thus improve the diversity of the algorithm. Second, we introduce a selective evolution mechanism. The optimal paths of each iteration are compared with the historical optimal paths to evolve relatively better path fragments, thus improving the convergence speed of the algorithm. Finally, when the algorithm stagnates, we introduce a dynamic shaking mechanism, which is able to dynamically dither the pheromone on the current optimal path according to the number of iterations, increasing the probability that the algorithm chooses other paths, thus helping to jump out of the locally optimal solution. In order to verify the effectiveness of the improved algorithm in this paper, we experimentally verify through a large number of TSP instances that SGHACA is able to improve the convergence of the algorithm and the accuracy of the solution to a certain extent when solving large-scale TSP. Meanwhile, compared with the latest improved algorithms, the accuracy of the solution of SGHACA is better than most of the latest improved algorithms, which indicates that the improved algorithm of this paper has some competitiveness. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes. [ABSTRACT FROM AUTHOR]

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings. [ABSTRACT FROM AUTHOR]

Copyright of EMPIRIA: Revista de Metodología de Ciencias Sociales is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remote sensing images can provide direct and accurate feedback on urban surface morphology and geographic conditions. They can be used as an auxiliary means to collect data for current geospatial information systems, which are also widely used in city public safety. Therefore, it is necessary to research remote sensing images. Therefore, we adopt the multi-threshold image segmentation method in this paper to segment the remote sensing images for research. We first introduce salp foraging behavior into the continuous ant colony optimization algorithm (ACOR) and construct a novel ACOR version based on salp foraging (SSACO). The original algorithm's convergence and ability to avoid hitting local optima are enhanced by salp foraging behavior. In order to illustrate this key benefit, SSACO is first tested against 14 fundamental algorithms using 30 benchmark test functions in IEEE CEC2017. Then, SSACO is compared with 14 other algorithms. The experimental results are examined from various angles, and the findings convincingly demonstrate the main power of SSACO. We performed segmentation comparison studies based on 12 remote sensing images between SSACO segmentation techniques and several peer segmentation approaches to demonstrate the benefits of SSACO in remote sensing image segmentation. Peak signal-to-noise ratio, structural similarity index, and feature similarity index evaluation of the segmentation results demonstrated the benefits of the SSACO-based segmentation approach. SSACO is an excellent optimizer since it seeks to serve as a guide and a point of reference for using remote sensing image algorithms in urban public safety. [ABSTRACT FROM AUTHOR]

Simple Summary: Ants fulfill an important ecological function, not only serving as one of the main decomposers but also playing a role in improving soil fertility. Ants may conflict with humans as a pest species in homes and living facilities (e.g., closets, wardrobes, cabinets, and tables), as well as in outdoor facilities (e.g., tables, chairs, and benches). In addition, ants and humans can come into conflict in green spaces (including parks, gardens, and tourist attractions) and can seriously interfere with outdoor leisure and the life and entertainment activities of people (especially children). Therefore, it is a matter of public health and environmental protection to ensure ant prevention by adopting green control technology and not merely by spraying pesticides to kill them. This review mainly focuses on the species and diversity of ants, especially in China, and the research progress of ant tropism regarding phototaxis, chromotaxis, and chemotaxis (i.e., 3-tropisms). Moreover, current research on repellent substances is also summarized, analyzed, and discussed in order to help the application of green prevention and control technology for ant diversity protection and conservation, especially in daily home life and entertainment activities. This field of research is related to ensuring the healthy life of the public and the protection of the ecological environment. Ants are one of the largest insect groups, with the most species and individuals in the world, and they have an important ecological function. Ants are not only an important part of the food chains but are also one of the main decomposers on the Earth; they can also improve soil fertility, etc. However, some species of ants are harmful to human beings, which leads to people's panic or worry about coming into contact with these insects during their daily home life or in their tourism or leisure activities. The presence of ants in indoor living facilities and in outdoor green spaces, parks, gardens, and tourist attractions seriously interferes with the leisure life and entertainment activities of all people (especially children). How can we control ants in these environments? Do we kill them by spraying insecticides, or do we adopt green prevention and control technology for the ecological management of ants? This topic is related to healthy life for the public and the protection of the ecological environment. In this paper, the species and diversity of ants are introduced, and research progress regarding ant tropism is introduced according to the three aspects of phototaxis, chromotaxis, and chemotaxis (i.e., "3-tropisms"). The research on repellent substances from plants and insects and the related ant attractants are also summarized, analyzed, and discussed, in order to help the research and application of green prevention and control technology for ant diversity protection and conservation. [ABSTRACT FROM AUTHOR]

Artificial intelligence has recently been used in FANET-based routing strategies for decision-making, which is a unique paradigm. For effective communication in flying vehicles that use routing protocols to accomplish tasks collectively, aerial vehicles are used in both civic and military applications. Aerial ad hoc networks are wirelessly connected, and designing routing schemes is difficult due to the rapid mobility. Ground base stations and satellites are frequently used to interconnect UAV ad hoc networks. This paper developed a novel routing protocol with a focus on ant behavior routing, which assists in end-to-end security. For the first time in flying networks, the column mobility model is used to evaluate the performance of routing protocols. While merging with aerial ad hoc networks, AI-based networking is a relatively new field. In simulation results, AntHocNet shows better results in comparison with other contemporary routing algorithms. Pheromone update process is used for data encryption in AntHocNet. This research study is performed on network simulator-2. [ABSTRACT FROM AUTHOR]

In this paper, we present a way to create an accurate trimap. Since the matting problem is a serious under-constrained problem, the user is required to provide additional constraint information to estimate the alpha value of the mixed pixels. The smaller the area of the unknown region of the user-provided trimap, the more accurate the alpha value of the estimated mixed pixels. But manually creating the trimap is a complicated and time-consuming task, and it is even impractical to manually create trimaps for some tasks. We use the ant colony algorithm to determine the boundary information of the foreground objects, and fuse different pheromone images at the superpixel level to create an accurate trimap. The trimap generated by our method also has higher precision when the edge of the foreground object has a lot of fine hair. Experiments show that the high-quality trimap can be generated by the method of this paper, which can effectively improve the performance of the matting algorithm and achieve accurate alpha mask estimation. [ABSTRACT FROM AUTHOR]

This paper follows a flat ontology of actor network theory to trace the social as an interconnected web of relations that do not necessarily cohere. Tea is an essential actant in the tea ceremony, but tea itself is its own web. This paper works with both the concept and the empirical case (tea) of materiality, trying to bring them into conversation. I propose an empirical-theoretical assemblage that does not follow a linear and smooth explanatory narrative. It aims to provide one among many webs of relations connected to tea: how the making process of tea affects the taste of tea, which is a complicated process involving human and non-human factors; the aging process of tea, which speaks to the agentic quality of object; how the taste of tea affects people's emotional and affective state. The competing theoretical discourses on materiality are brought together by the ANT approach and the specific associations of tea enrich our understanding of the theoretical literatures and food studies. [ABSTRACT FROM AUTHOR]

The paper considers the modification and application of the ant colony method for the problem of directed enumeration of the values of system parameters when performing calculated multiple calculations. Interaction with the user makes it possible to stop the process of exhaustive enumeration of sets of parameter values, and the application of a modification of the ant colony method will allow us to consider rational sets at early iterations. If the user does not terminate the algorithm, then the proposed modifications allow one to enumerate all solutions using the ant colony method. To modify the ant colony method, a new probabilistic formula and various algorithms of the ant colony method are proposed, allowing for each agent to find a new set of parameter values. The optimal algorithm, according to the research results, is the use of repeated endless cyclic search for a new solution. This modification allows you to consider all solutions, and at the same time, find all the optimal solutions among the first 5% of the considered solutions. [ABSTRACT FROM AUTHOR]

Leaf-cutting ants are insects of great economic importance which cause economic losses in numerous crops. Compounds that modulate behaviour, and among these natural repellents, show promise in integrated pest management strategies. Tea tree oil has been found to be repellent to certain insect pests, so this study aimed at identifying the effect of tea tree on foraging behaviour in Acromyrmex spp. ants in laboratory as well as in field trials. In laboratory bioassays, a repellent dose response effect of tea tree oil was observed, where concentrations of 0.0001 to 0.1% tea tree did not affect ant behaviour, but concentrations of 1 and 10% repelled ants. In the laboratory, the repellent effect of tea tree was lost after four days and its effect on ant behaviour was evident only at very short distances (less than 1 cm). In the field, ants avoided walking through filter papers treated with tea tree at 1% placed on the foraging trails, and food resources surrounded by tea tree at 1% were not collected. Tea tree is a potent short term leaf-cutting ant repellent and attractive food sources could be protected from leaf-cutting ants attack by this oil. [ABSTRACT FROM AUTHOR]

Intensive livestock production might have a negative environmental impact, by producing large amounts of animal excrements, which, if not properly managed, can contaminate nearby water bodies with nutrient excess. However, if animal manure is exported to distant crop fields, to be used as organic fertilizer, pollution can be mitigated. It is a single-objective optimization problem, in regards to finding the best solution for the logistics process of satisfying nutrient crops needs by means of livestock manure. This paper proposes a dynamic approach to solve the problem, based on a decentralized nature-inspired cooperative technique, inspired by the foraging behavior of ants (AIA). Results provide important insights for policy-makers over the potential of using animal manure as fertilizer for crop fields, while AIA solves the problem effectively, in a fair way to the farmers and well balanced in terms of average transportation distances that need to be covered by each livestock farmer. Our work constitutes the first application of a decentralized AIA to this interesting real-world problem, in a domain where swarm intelligence methods are still under-exploited. [ABSTRACT FROM AUTHOR]

Music resources are characterized by quantization, diversification and complication. With the rapid increase of the demand for music resources, the storage of music resources is very large. In order to improve the retrieval effect of music resources, a massive music resources retrieval method based on ant colony algorithm is proposed to effectively use music resources. This paper constructs autocorrelation function to extract pitch feature of music resource, classifies the music resource information by calculating feature similarity. Using ant colony algorithm to correlate the feature of music resource, gain the result of correlative, locate the result of detection and get the result of multi-module. Simulation results show that the proposed method has high precision and recall, short retrieval time and can effectively retrieve massive music resources. [ABSTRACT FROM AUTHOR]

Deep learning (DL) based detection models are powerful tools for large-scale analysis of dynamic biological behaviors in video data. Supervised training of a DL detection model often requires a large amount of manually-labeled training data which are time-consuming and labor-intensive to acquire. In this paper, we propose LFAGPA (Learn From Algorithm-Generated Pseudo-Annotations) that utilizes (noisy) annotations which are automatically generated by algorithms to train DL models for ant detection in videos. Our method consists of two main steps: (1) generate foreground objects using a (set of) state-of-the-art foreground extraction algorithm(s); (2) treat the results from step (1) as pseudo-annotations and use them to train deep neural networks for ant detection. We tackle several challenges on how to make use of automatically generated noisy annotations, how to learn from multiple annotation resources, and how to combine algorithm-generated annotations with human-labeled annotations (when available) for this learning framework. In experiments, we evaluate our method using 82 videos (totally 20,348 image frames) captured under natural conditions in a tropical rain-forest for dynamic ant behavior study. Without any manual annotation cost but only algorithm-generated annotations, our method can achieve a decent detection performance (77% in F 1 score). Moreover, when using only 10% manual annotations, our method can train a DL model to perform as well as using the full human annotations (81% in F 1 score). [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε -greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence. [ABSTRACT FROM AUTHOR]

Abstract. Many ants use pheromone trails to organize collective foraging. This study investigated the rate at which a well-established Pharaoh's ant, Monomorium pharaonis (L.), trail breaks down on two substrates (polycarbonate plastic, newspaper). Workers were allowed to feed on sucrose solution from a feeder 30 cm from the nest. Between the nest and the feeder, the trail had a Y-shaped bifurcation. Initially, while recruiting to and exploiting the feeder, workers could only deposit pheromone on the branch leading to the feeder. Once the trail was established (by approximately 60 ants per min for 20 min), the ants were not allowed to reinforce the trail and were given a choice between the marked and unmarked branches. The numbers of ants choosing each branch were counted for 30 min. Initially, most went to the side on which pheromone had been deposited (80% and 70% on the plastic and paper substrates, respectively). However, this decayed to 50% within 25 min for plastic and 8 min for paper. From these data, the half-life times of the pheromone are estimated as approximately 9 min and 3 min on plastic and paper, respectively. The results show that, for M. pharaonis , trail decay is rapid and is affected strongly by trail substrate. [ABSTRACT FROM AUTHOR]

Ant colony optimization has insufficient convergence and tends to fall into the local optima when solving the traveling salesman problem. This paper proposes a co-evolutionary multi-colony ant colony optimization (MCGACO) to overcome this deficiency and applies it to the Robot Path Planning. First, a dynamic grouping cooperation algorithm, combined with Ant Colony System and Max-Min Ant System, is introduced to form a heterogeneous multi-population structure. Each population co-evolves and complements each other to improve the overall optimization performance. Second, an adaptive guidance mechanism is proposed to accelerate convergence speed. The mechanism includes two parts: One is a dynamic evaluation network, which is used to evaluate and divide all solutions by the evaluation function. The other is a positive-negative incentive strategy, which can enhance the guiding role of solutions with higher evaluation value. Besides, to jump out of the local optima, an inter-specific co-evolution mechanism based on the game model is proposed. By dynamically determining the optimal communication combination, the diversity among populations can be well balanced. Finally, the experimental results demonstrate that MCGACO outperforms in terms of solution accuracy and convergence. Meanwhile, the proposed algorithm is also feasible for application in Robot Path Planning. [ABSTRACT FROM AUTHOR]

Ant Colony Optimization (ACO) tends to fall into local optima and has insufficient convergence when solving the Traveling Salesman Problem (TSP). To overcome this problem, this paper proposes a multiple ant colony optimization (LDTACO) based on novel Long Short-Term Memory network and adaptive Tanimoto communication strategy. Firstly, we introduce an Artificial Bee Colony-based Ant Colony System (ABC-ACS), which along with the classic Ant Colony System (ACS) and Max-Min Ant System (MMAS), form the final proposed algorithm. These three types of subpopulations complement each other to improve overall optimization performance. Secondly, the evaluation reward mechanism is proposed to enhance the guiding role of the Recommended paths, which can effectively accelerate convergence speed. Besides, an adaptive Tanimoto communication strategy is put forward for interspecific communication. When the algorithm is stagnant, the homogenized information communication method is activated to help the algorithm jump out of the local optima, thus improving solution accuracy. Finally, the experimental results show that the proposed algorithm can lead to more accurate solution accuracy and faster convergence speed. [ABSTRACT FROM AUTHOR]

Simple Summary: Adhesive tapes are as effective as baits and pitfall traps in monitoring Solenopsis invicta and Tapinoma melanocephalum in urban areas in southern China. However, adhesive tapes detected less ants and fewer species of non-target ants than baits and pitfall traps. Our study demonstrated that tape-paving behavior can potentially be used in developing more specific monitoring methods for S. invicta and T. melanocephalum in urban areas in southern China. Our previous study discovered that two urban pest ants, red imported fire ants, Solenopsis invicta Buren (Formicidae: Myrmicinae), and ghost ants, Tapinoma melanocephalum (Fabricius) (Formicidae: Dolichoderinae), can pave viscose surfaces with particles to facilitate food search and transport. We hypothesize that this paving behavior can be applied to monitor S. invicta and T. melanocephalum. In the present study, 3998 adhesive tapes, each with a food source (sausage), were placed in 20 locations around Guangzhou, China (181–224 tapes per location), and their efficiency to detect S. invicta and T. melanocephalum was compared with two traditional ant-monitoring methods, baiting and pitfall trapping. Overall, S. invicta was detected by 45.6% and 46.4% of baits and adhesive tapes, respectively. In each location, the percentage of adhesive tapes detecting S. invicta and T. melanocephalum was similar when compared to baits and pitfall traps. However, significantly more non-target ant species showed up on bait and pitfall traps. Seven non-target ant species—Pheidole parva Mayr (Formicidae: Myrmicinae), Pheidole nodus Smith (Formicidae: Myrmicinae), Pheidole sinica Wu & Wang (Formicidae: Myrmicinae), Pheidole yeensis Forel (Formicidae: Myrmicinae), Carebara affinis (Jerdon) (Formicidae: Myrmicinae), Camponotus nicobarensis Mayr (Formicidae: Formicinae), and Odontoponera transversa (Smith) (Formicidae: Ponerinae)—also showed tape paving behavior, but they can be easily distinguished morphologically from S. invicta and T. melanocephalum. Our study showed that the paving behavior occurs in different subfamilies of ants (i.e., myrmicinae, dolichoderinae, formicinae, and ponerinae). In addition, paving behavior can potentially be used to develop more specific monitoring methods for S. invicta and T. melanocephalum in urban areas in southern China. [ABSTRACT FROM AUTHOR]

High current amounts, low voltage level and radial structure assign high percentage of active power loss in distribution system. There are many means in reducing the active power loss of distribution networks; this paper introduces an ant colony algorithm to solve the optimal network reconfiguration and capacitor placement problem for power loss reduction and voltage profile enhancement in distribution networks. The ant colony algorithm was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trial formation. Ants of artificial colony are able to search for the successively shorter feasible routes by using information accumulated in the form of a pheromone trial deposited on the edge of their traveling path. The proposed approach is demonstrated employing one distribution network. Computational results show that by taking into account feeder reconfiguration and capacitor placement simultaneously, one can be minimize losses more efficiently by considering them separately. [ABSTRACT FROM AUTHOR]

In the context of the normalization of the epidemic, contactless delivery is becoming one of the most concerned research areas. In the severe epidemic environment, due to the frequent encounter of bayonet temperature measurement, road closure, and other factors, the real-time change frequency of each traffic information is high. In order to improve the efficiency of contactless distribution and enhance user satisfaction, this paper proposes a contactless distribution path optimization algorithm based on improved ant colony algorithm. First of all, the possible traffic factors in the epidemic environment were analyzed, and the cost of each link in the distribution process was modeled. Then, the customer satisfaction is analyzed according to the customer service time window and transformed into a cost model. Finally, the total delivery cost and user satisfaction cost were taken as the optimization objectives, and a new pheromone updating method was adopted and the traditional ant colony algorithm was improved. In the experiment, the effectiveness of the proposed model and algorithm is verified through the simulation optimization and comparative analysis of an example. [ABSTRACT FROM AUTHOR]

The study on multiple unmanned aerial vehicles (UAVs) reconnaissance task allocation problem is an important research field, which is significant for both military and civilian applications. This problem has often been considered as a multiple traveling salesman problem where the targets are considered as points. In this paper, we present a novel mathematical model that classifies heterogeneous targets as point targets, line targets and area targets to improve the fidelity of the model. It is a complex combinatorial optimization problem, for which we can hardly get an optimal solution as the scale of the problem expands. A new heuristic algorithm called grouping ant colony optimization algorithm is proposed for this new model. Compared with traditional ant colony algorithm, pheromone is divided into membership pheromone and sequence pheromone corresponding to grouping and permutation characteristics of the model, respectively. Also, negative feedback mechanism is introduced to accelerate convergence speed of the algorithm. The simulation results demonstrate that the new algorithm can consider comprehensively the performance of different UAVs and the characteristic of heterogeneous targets. It outperforms existing methods reported in the literature in terms of optimality of the result, and the advantage gets more obvious with the scale of reconnaissance task allocation problem expanding. [ABSTRACT FROM AUTHOR]

Ant rescue behaviour belongs to the most interesting subcategories of prosocial and altruistic behaviour encountered in the animal world. Several studies suggested that ants are able to identify what exactly restrains the movements of another individual and to direct their rescue behaviour precisely to that object. To shed more light on the question of how precise the identification of the source of restraint of another ant is, we investigated rescue behaviour of red wood ant Formica polyctena workers, using a new version of an artificial snare bioassay in which a nestmate victim bore two wire loops on its body, one (acting as a snare) placed on its petiole and an additional one on its leg. The tested ants did not preferentially direct their rescue behaviour towards the snare. Moreover, the overall strategy adopted by the most active rescuers was not limited to precisely targeted rescue attempts directed towards the snare, but consisted of frequent switching between various subcategories of rescue behaviour. These findings highlight the importance of precise identification of cognitive processes and overall behavioural strategies for better understanding of causal factors underlying animal helping behaviour in light of new facts discovered by testing of various successive research hypotheses. [ABSTRACT FROM AUTHOR]

This paper is concerned with the attracting set of an ant colonies model with bounded noisy perturbation. This perturbation is modeled by the well-known Ornstein–Uhlenbeck process and the arc tangent function. For the random model, we first verify the existence and uniqueness of the global positive solution, and then prove the existence of the deterministic attracting set. Furthermore, in order to reveal more detailed information about the long-time behavior of the ant colonies system, we analyze the internal structure of the attracting set and provide some conditions under which coexistence (or extinction) of the ant species exists in the ant colonies system. [ABSTRACT FROM AUTHOR]

Simple Summary: Soldiers and workers collectively perform defensive behaviors in termite colonies. To determine the molecular mechanism driving the defensive behaviors of termite soldiers, we tested the effect of the juvenile hormone analogue pyriproxyfen (JHA) and the soldier caste-specific protein 1 (RaSsp1) on the defensive behaviors of Reticulitermes aculabialis soldiers when encountering an ant invasion. The results showed that JHA-feeding may affect defensive behaviors (i.e., bites and head-banging) of soldiers via downregulation of RaSsp1 expression. Feeding JHA may also affect the content of alarm pheromones in soldiers, including limonene, which also influenced the defensive behavior of soldiers. These results suggest that JH and RaSsp1 may play important roles in modulating social defense in termite colonies. Eusocial insects have evolved specific defensive strategies to protect their colonies. In termite colonies, soldiers perform a colony-level defense by displaying mechanical biting, head-banging and mandible opening–closing behaviors. However, few studies have been reported on the factors modulating defensive behaviors in termites. Owing to JH (juvenile hormone) being involved in soldier differentiation, JH was speculated to affect defensive behaviors in termite soldiers. To determine the effect of JH on the defensive behaviors of termite soldiers, we performed a JHA-feeding and RaSsp1-silencing experiment and then tested the changes in defense-related behaviors, alarm pheromones and key JH signaling genes. The observed result was that after feeding workers with JHA, soldiers displayed the following: (1) decreased biting events and increased head-banging events; (2) a reduced expression of RaSsp1 and increased expression of Met (methoprene-tolerant, the nuclear receptor of JH) and Kr-h1 (the JH-inducible transcription factor Krüppel homolog 1); and (3) a decreased concentration of alarm pheromones, including α-pinene, β-pinene and limonene (+, −). Further study showed that soldiers silenced for RaSsp1 also exhibited (1) decreased biting events and increased head-banging events and (2) increased expression of Met and Kr-h1. In addition, soldiers stimulated by the alarm pheromone limonene displayed an increase in the frequency of mandible opening–closing and biting behavior. All of these results show that JHA influenced the defensive behaviors of termite soldiers, possibly via downregulating RaSsp1 expression, up-regulating Met and Kr-h1 and stimulating the secretion of alarm pheromones, suggesting that the JH pathway plays important roles in modulating social behaviors in termite colonies. [ABSTRACT FROM AUTHOR]

In the present study, we tested associative olfactory learning performance of the ant Formica clara in laboratory conditions based on their MaLER responses. Ants were harnessed with the help of a paper-made holder and conditioned in two groups by forward pairing either limonene or eugenol (conditioned stimuli – CS) with a reward (unconditioned stimulus – US). After conditioning, memory tests were performed for each CS 15, 60 and 90 minutes following the conditioning in the absence of US. A further test was performed for each group with the CS of the other group to see if the ants showed an odor generalization. The frequencies of MaLER-positive responses during tests were used as indication of learning performance. Ants learned the association between the CS and US during conditioning for both odors and showed initially significant and gradually decreasing MaLER-positive responses during tests. The results showed that conditioning to the odors was absolute and no generalization was observed. We conclude that associative learning is a universal strategy for ants to adjust their behaviors to environmental changes around them and the MaLER technique is a powerful and easy to apply technique to test learning and memory capacities in ants. [ABSTRACT FROM AUTHOR]

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice. [ABSTRACT FROM AUTHOR]

To solve the problem of falling into local optimum and poor convergence speed in large Traveling Salesman Problem (TSP), this paper proposes a Pearson correlation coefficient-based Pheromone refactoring mechanism for multi-colony Ant Colony Optimization (PPACO). First, the dynamic guidance mechanism is introduced to dynamically adjust the pheromone concentration on the path of the maximum and minimum spanning tree, which can effectively balance the diversity and convergence of the algorithm. Secondly, the frequency of communication between colonies is adjusted adaptively according to a criterion based on the similarity between the minimum spanning tree and the optimal solution. Besides, the pheromone matrix of the colony is reconstructed according to the Pearson correlation coefficient or information entropy to help the algorithm jump out of the local optimum, thus improving the accuracy of the solution. These strategies greatly improve the adaptability of the algorithm and ensure the effectiveness of the interaction. Finally, the experimental results indicate that the proposed algorithm could improve the solution accuracy and accelerate the convergence speed, especially for large-scale TSP instances. [ABSTRACT FROM AUTHOR]

The ant colony optimization (ACO) is inspired by the behavior of real ants, and as a bioinspired method, its underlying computation is massively parallel by definition. This paper shows re-engineering strategies to migrate the ACO algorithm applied to the Traveling Salesman Problem to modern Intel-based multi- and many-core architectures in a step-by-step methodology. The paper provides detailed guidelines on how to optimize the algorithm for the intra-node (thread and vector) parallelization, showing the performance scalability along with the number of cores on different Intel architectures, reporting up to 5.5x speedup factor between the Intel Xeon Phi Knights Landing and Intel Xeon v2. Moreover, parallel efficiency is provided for all targeted architectures, finding that core load imbalance, memory bandwidth limitations, and NUMA effects on data placement are some of the key factors limiting performance. Finally, a distributed implementation is also presented, reaching up to 2.96x speedup factor when running the code on 3 nodes over the single-node counterpart version. In the latter case, the parallel efficiency is affected by the synchronization frequency, which also affects the quality of the solution found by the distributed implementation. [ABSTRACT FROM AUTHOR]

Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there is great interest in learning which environmental and behavioral factors drive ant trophallaxis. In this paper, we analyze ant trophallaxis behavior in a colony of 73 carpenter ants, observed at 1-s intervals over a period of 4 h. The data represent repeated observations from a dynamic contact network; however, traditional statistical analyses of network models are ill-suited for data observed at such high temporal resolution. We present a model for high-resolution longitudinal network data, where the network is assumed to be a time inhomogeneous, continuous-time Markov chain, with transition rates modeled as a function of time-varying individual and pairwise biological covariates. In particular, the high temporal resolution of the data leads to a tractable likelihood function, and likelihood-based inference procedures are utilized to explain which biological factors drive contact. Our results reveal how differences in ant social castes and individual behaviors, such as ant speed and activity levels, influence patterns of ant trophallaxis in the colony. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

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

The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning. [ABSTRACT FROM AUTHOR]

USVs have emerged as effective instruments in a wide range of maritime applications, including environmental monitoring and search-and-rescue missions. The surge in their application is attributed to their operational flexibility and cost efficiency, making them a focal point of research interest in recent years. One of the research angles related to the USV is the path planning algorithm, which is the core technology in USV deployment. Among various path planning algorithms, Ant Colony Optimization (ACO) has garnered considerable attention for its capability to discover near-optimal solutions in complex search spaces inspired by the foraging behavior of ants. Therefore, this paper discusses the application of ACO in the path planning of USVs, including the development of the ACO algorithms and the challenges faced in designing the path planning algorithm. This work also proposes research priorities and future works for USV path planning based on existing research works. Specifically, future research works aim to address the existing challenges and enhance the practical deployment of ACO-based path planning techniques for USVs. In summary, this paper is a valuable resource for researchers and engineers interested in optimizing the navigation of USVs through effective path planning strategies using ACO algorithms. • Implementing ACO-based path planning in the dynamic environment is the main challenge. • There has been limited research on multi-objective path planning for USVs. • Collaboration mission between multiple USVs and UUVs can be explored in the future. • Only 14.7% of the ACO-based path planning in USVs works include the hardware validation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-colony ant optimization based on pheromone fusion mechanism of cooperative game (CGMACO) to balance the convergence speed and diversity of the algorithm. Firstly, the heterogeneous multi-colony is composed of ant colony system (ACS) and Max–Min ant system (MMAS), and these two classical colonies coordinate together to improve the solution quality. Secondly, the cooperative game model determines which sub-colonies can interact with each other based on evaluating each union's payoff, while the pheromone fusion mechanism decides what information can exchange by regulating the pheromone matrix of each subpopulation. Those two methods can greatly diversify the solution of algorithm. In addition, the information entropy is also introduced to control the interaction frequency, which enhances the adaptability of the algorithm. Finally, the experimental results of the large-scale TSP instances show that the improved algorithm can improve the accuracy of the solution without affecting the convergence speed and better than the existing intelligent algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a method to assess the state of convergence of Ant Colony Optimization algorithms (ACO) using network science metrics. ACO is a computational intelligence technique inspired by the behavior of ants in nature, and it is commonly used to solve combinatorial optimization problems. In the ACO, artificial ants seek for solutions to the tackled problem, transversing the graph that represents the combinatorial problem. For each displacement, the ants deposit pheromone on the edges which they passed. The ants use the pheromone for indirect communication, and the amount of pheromone weighs the intensity of interaction between the ants. The graph of pheromones forms a network that evolves along the iterations. Network Science allows studying the structure and the dynamics of networks. This area of study provides metrics used to extract global information from networks in a particular moment. This paper aims to show that two network science metrics, the Clustering coefficient, and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO. We analyze the convergence of the four variations of the ACO in the Traveling Salesman Problem (TSP). Based on the obtained results, we demonstrate that it is possible to evaluate the convergence of the ACO for the TSP based on the proposed metrics, especially the adapted clustering coefficient. [ABSTRACT FROM AUTHOR]

Purpose - Search engines are a dominant part of our everyday activities and lives. These tools support decision-making, play a crucial role in constructing knowledge, and have a significant impact on our individual and social behaviour. The paper is aimed at the search engine bias problem as one of the important ethical issues associated with search technology algorithmic design and development. Design/Methodology/Approach - Conceptual analytical method and critical approach are applied to a problem of search engine bias. Based on analysis, typology of specific problems and solutions are summarized and characterized. ANT (actor-network theory) (Latour, 2005) was used to introduce model consisting of search engine bias problem actors present in a complex bidirectional relations. Results - The focus of this conceptual article is mainly on search engine bias in connection with manipulation techniques such as SEO (search engine optimization) methods and paid results. Problems of Google as a gatekeeper, personalization, and biased algorithmic design are also further analysed in more detail. As a result of the analysis, possible solutions to the search engine bias problem are categorized and discussed. Originality/Value - This work provides new insights into search engine bias problem in the context of recent technological development and trends. The current methods, principles, and frameworks as a solution to the ethical issues of search engine technology are summarized. [ABSTRACT FROM AUTHOR]

Simple Summary: Florida carpenter ants (Camponotus floridanus) live in colonies comprised of thousands of workers. The smallest workers, known as minors, engage in routine tasks such as nursing and foraging while the largest workers, known as majors, are thought to be soldiers specialized for defending the nest. How ant colonies allocate their workforce to address the dynamic and ever-changing needs of the colonies remains an open question in the field, but current evidence suggests that ant social behavior likely results from a combination of genetic/epigenetic, physiological, and systems-level processes. Here, we extend these studies by investigating the role of olfactory sensitivity in regulating ant behavior. Minor workers exhibited significant shifts in olfactory sensitivity and odor coding as they aged and switched tasks. The olfactory sensitivity of majors, however, remained relatively stable as they aged. From these studies, we also identified a single compound, 3-methylindole, which elicited significantly higher olfactory responses and aversive behavior in nurses compared to foragers, suggesting that this chemical may have a role in brood care. Overall, these studies support the hypothesis that changes in olfactory sensitivity play an important role in regulating social behavior in ants. Age-related changes in behavior and sensory perception have been observed in a wide variety of animal species. In ants and other eusocial insects, workers often progress through an ordered sequence of olfactory-driven behavioral tasks. Notably, these behaviors are plastic, and workers adapt and rapidly switch tasks in response to changing environmental conditions. In the Florida carpenter ant, smaller minors typically perform most of the work needed to maintain the colony, while the larger majors are specialized for nest defense and rarely engage in these routine tasks. Here, we investigate the effects of age and task group on olfactory responses to a series of odorant blends in minor and major worker castes. Consistent with their respective roles within the colony, we observed significant age-associated shifts in the olfactory responses of minors as they transitioned between behavioral states, whereas the responses of majors remained consistently low regardless of age. Furthermore, we have identified a unitary compound, 3-methylindole, which elicited significantly higher responses and behavioral aversion in minor nurses than in similarly aged foragers suggesting that this compound may play an important role in brood care. Taken together, our results suggest that age- and task-associated shifts in olfactory physiology may play a critical role in the social organization of ant colonies. [ABSTRACT FROM AUTHOR]

Rüdiger Wehner's work on insect orientation and navigation has influenced many scientists studying navigation, not only in ants and bees, but in other animals as well. We review the scientific legacy of six topics arising from Wehner's work on navigation. The polarisation compass is a chapter with a lot of behavioural and neurobiological detail. It has influenced the study of polarisation vision in other systems, and led Wehner to formulate the concept of a matched filter. The matched filter has probably had earlier formulations, but Wehner's paper on it has been much cited in studies on navigation and in other fields. The polarisation compass serves the task of path integration in insects. Work on path integration took off in the 1980s with work on desert ants and rodents. The use of terrestrial visual cues, landmarks or the panorama in view-based matching is another major theme of navigational research today. Search strategies were also well described in desert ants, and this line of research helped to launch theoretical and empirical developments in searching behaviour, now a lively area of research. Finally, robotic work has often drawn inspiration from work on insect navigation. We end with some discussion of current research directions. [ABSTRACT FROM AUTHOR]