Pheromone Model Research Articles

Temperature-responsive microcapsule hydrogel fabricated by pickering emulsion polymerization for pheromones application

Insect pheromone is a natural effective biological pesticide. To improve the utilization efficiency of pheromone pesticides and reduce labor costs, a sustained and controlled release preparation coordinating with the circadian rhythm of insects has been proposed by our previous report. However, that preparation has some deficiencies, such as the loading capacity of pheromone and sustained time. In order to overcome the existent shortcomings, a temperature-responsive release microcapsule hydrogel loaded with myrcene (as a pheromone model) was fabricated by Pickering emulsion polymerization using modified SiO2 nanoparticles as a stabilizer of microcapsule wall, octadecane and Poly(N-isopropylacrylamide) (PNIPAM) separately as temperature responders of microcapsule core and wall in the present study. The morphological structure and thermal performance of the microcapsule hydrogel were characterized. The release behaviors of myrcene at different environmental temperatures and alternating temperature cycles were also tested. The results indicated that myrcene was successfully loaded in the Myrcene/Octadecane@SiO2-PNIPAM (MOSP) microcapsule hydrogel with encapsulation efficiency and loading capacity of 80.3 % and 13.3 %, respectively. The results of the myrcene release under simulated diurnal temperature cycles revealed that the cumulative release amount was only about 43 % during 15 cycles and release rates respectively were about 0.79 μg/h at 35 °C and 0.058 μg/h at 25 °C, which demonstrated the persistent temperature sensitivity and good temperature rhythm remained after 15 cycles. These results suggested that the MOSP microcapsule hydrogel had a good controlled release effect on myrcene and could adapt well to the circadian rhythm activity of some insects. Therefore, this study provided a promising strategy to improve the utilization efficiency of pheromones through the combination of octadecane and PNIPAM as pheromones release controllers.

Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

ACO-based traffic routing method with automated negotiation for connected vehicles

Most traffic control systems are centralized, where all the collected data can be analyzed to make a decision. However, there are problems with computational complexity and, more seriously, real-time decision-making. This paper proposes a decentralized traffic routing system based on a new pheromone model of ant colony optimization algorithm and an automated negotiation technique in a connected vehicle environment. In particular, connected vehicles utilize a new pheromone model, namely the inverted pheromone model, which generates a repulsive force between vehicles and gives negative feedback to the congested roads. They also perform a collective learning-based negotiation process for distributing traffic flows throughout the road networks, reducing traffic congestion. Via extensive simulations based on the Simulation of Urban Mobility, the proposed system shows that it can significantly reduce travel time and fuel consumption compared to existing systems.

A Study on the Relationship between Artificial Intelligence and 5G Network Construction and the Level of Economic Development of Regional Cities

Urban digital governance represented by digital city and smart city is becoming an important direction of urban agglomeration development. It is necessary to reexamine the internal relationship mechanism between local government digital action and regional governance behavior. Based on the theory of collective action and network governance, this paper discusses how the economic development level of local government interacts with the cooperative network status of urban agglomeration and affects the tendency of digital governance strategy of local government. By constructing the configuration matrix of “economic development level” and “cooperative network status” of local governments in urban agglomerations, this paper extracts the possible “center” and “follow” governance strategies that local governments in different configurations may adopt in the digital field. The ant colony optimization algorithm proposed in this paper consists of two parts—ACO initialization and ACO optimization. ACO initialization describes how to map the path of ants to the solution of the problem, that is, the combination of orders. In addition, ACO initialization will also define and initialize the pheromone and heuristic information model compatible with the solution. ACO optimization updates the pheromone model to heuristic guide ants to build paths, so as to iteratively find the optimal solution. The case study of 26 cities in the Yangtze River Delta of China shows that most of the digital governance strategies of local governments are in line with the selection logic of their configuration status of “economic level network status”. The study results proved that the proposed model has provided effective accuracy of 96%. However, driven by selective returns, there are still some “abnormal” governance tendencies.

Affirmative Ant Colony Optimization Based Support Vector Machine for Sentiment Classification

Sentiment analysis is part of contextual text mining, which detects, extracts and supports an organization in understanding their brand or service in social sentiment while monitoring the reviews provided by customers in online shops. The rise of online shopping and digitalization is practically achieved, and the quality of products is tough for users to judge. There is no model to find out about the same or unlike a set of people with similar sentiment analysis concerning online product evaluations. In this paper optimization-based classification algorithm is proposed namely, Affirmative Ant Colony Optimization Based Support Vector Machine (AACOSVM) to classify sentiments provided by customers in online shopping. This paper provides a new Ant Colony Optimization method via providing a novel pheromone model for support vector machine optimization parameters in two steps. The first one is statute of state transition, and the second step is statute of state updates. They aim to allow the ants to use the fake pheromone path to pick parameters and to motivate ants to create subsets having the least classification mistakes. The proposed work includes product review datasets from Amazon to assess the performance of the AACOSVM against existing classifiers, namely, Entropy-Based Classifier (EBC) and Enhanced Feature Attention Network (EFAN). Various review datasets are accessible at Amazon for various items. This research effort has identified a dataset from DVDs, books, kitchen appliances and electronics from the many multiple available review datasets. It utilizes the natural foraging behavior of ants towards searching for food to identify and classify the sentiments present in the product reviews. AACOSVM is evaluated using two standard data mining performance metrics, namely F-Measure and Classification Accuracy. Results indicate that the proposed classification algorithm AACOSVM achieves better F-Measure and Classification Accuracy than the EBC and EFAN classifiers.

Data stream classification with ant colony optimisation

Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACO-based algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures—for example, heuristic calculation, selection of continuous attributes, pruning—require multiple passes through the data to create a model. In this paper, we present a new ACO-based algorithm for data stream classification. The proposed algorithm, called Stream Ant-Miner (sAnt-Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed-type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt-Miner produces statistically significant concise models compared with state-of-the-art rule induction data stream algorithms, without negative effects on their predictive accuracy.

Pheromone enclosed in halloysite with n-octadecane releases rhythmically under simulated diurnal temperature

Insect pheromone is a class of natural non-toxic and harmless biological pesticide. To improve the utilization efficiency of the pheromone pesticides, controlled release in response to circadian rhythm of insects is a feasible approach because some insects act and release pheromones with a circadian rhythm. Thus, myrcene, as the insect pheromone model, was first loaded into the hollow nano-cavity of halloysite nanotubes (Hal), which were further enclosed with melted n-octadecane as a coating to form a composite (PMHal). The latter was simply blended into chitosan solution to cast a chitosan composite film (PMHal@CS) for preventing n-octadecane leakage during the phase change process. The effects of temperature change on the release rate of myrcene from the composite film were evaluated. Results showed the combination of Hal with n-octadecane could significantly slow the release rate of myrcene from the composite film when the ambient temperature was below 15 °C. If the ambient temperature was near to the melting point of n-octadecane, the release rate of myrcene would be enhanced. In alternating temperature cycles test of PMHal@CS film, the average release rate of myrcene was 0.65 μg/h at 30 °C and 0.022 μg/h at 15 °C, the persistent temperature sensitivity and good temperature rhythm were still remained after 7 cycles. These results suggested that the composite film had a good controlled release effect on myrcene and could adapt well to the circadian rhythm activity of some insects.

Prior exposure to Hedione, a model of pheromone, does not affect female ratings of male facial attractiveness or likeability

The existence of pheromones in humans is controversial, partly because of definitional difficulties and partly because of the question of possible chemical substances. The synthetic compound Methyl dihydrojasmonate (Hedione) is potent to bind to vomeronasal-type 1 receptors (VN1R1s) and activate limbic areas of the brain in a sex-specific manner. However, one of the most important definitional points for a human pheromone effect has not yet been investigated, i.e., whether smelling Hedione, a model of pheromone, has a behavioral effect. We tested in females whether Hedione leads to altered perception of male social stimuli. Each of the included women were sensitive to Hedione and were tested around the time of ovulation in three consecutive sessions, during each they were exposed to either Hedione or Phenylethyl alcohol or Odorless air. We measured the speed of male face recognition (implicit priming task) and collected ratings of facial attractiveness and likeability of men (explicit task). Only about half of the women tested were sensitive to Hedione. Those women did not show any effect of Hedione exposure in the implicit priming task and moderate, but non-significant effects in the explicit task. We therefore assume that Hedione is not a potent model of pheromone in humans and this observation may be due to the fact that the artificially produced substance is not suited for signaling the proximity of other humans. Furthermore, the high rate of Hedione-specific anosmia leads to the hypothesis that a substantial proportion of individuals has a poor V1NR1 receptor expression.

A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network

Swarm robotics is an emerging interdisciplinary field that has many potential real-world applications. Swarm robotics aims to produce robust, scalable, and flexible self-organizing behaviors through local interactions from a large number of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of a single neuron corresponds to the density of a pheromone, which diffuses to neighboring neurons through their local connections. A neural network is updated based on the proposed evaporation model. Neural networks can often mimic the dynamics and features of pheromones. Therefore, in this work, we develop an optimization method to determine the key parameters of cooperative foraging based on mathematical modeling. The differential equation variables represent the number of foraging robots assigned different tasks. The solutions of the differential equations represent the dynamics of the foraging behavior. The key parameters that affect task allocation are determined to make optimal decision rules. Simulation experiments are conducted under different foraging scenarios. The experimental results demonstrate the effectiveness of the proposed pheromone model.

Ant Colony Optimization With an Improved Pheromone Model for Solving MTSP With Capacity and Time Window Constraint

The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms.

An improved pheromone-based vehicle rerouting system to reduce traffic congestion

The growing number of vehicles necessitates the implementation of effective vehicle rerouting systems. Designing an effective vehicle rerouting system is challenging due to the dynamic nature of vehicular network. In this paper, a Proactive Travel-time based Pheromone Rerouting (PTPR) system is proposed. First, PTPR system predicts future congestion level using travel time and vehicle density information. Then, vehicles are distributed to multiple paths to balance the traffic load. Different from the existing pheromone-based rerouting systems, each ant (vehicle) in PTPR system can deposit its pheromone on multiple road segments away, instead of its direct adjacent road segment, based on its route. This new pheromone model aims to improve the performance of PTPR system. In addition, a localized dynamic k-shortest path (LDkSP) algorithm is proposed to reduce computational effort of PTPR system. Experiments were conducted on two different areas (i.e. suburban and urban) using Simulation of Urban Mobility (SUMO). Results show that the proposed PTPR system outperforms the existing rerouting system by reducing mean travel time, fuel consumption, and increasing number of arrive vehicles by 8.2%, 2%, and 15.1% respectively in Woodlands (suburban) and 28.7%, 17.2%, and 29.5% respectively in Novena (urban). The computation time used to reroute each vehicle is also reduced by 68.3% and 92.1% in suburban and urban area respectively using the proposed LDkSP. Finally, experiments over various usage rates and estimation errors showed that the proposed PTPR system is robust to usage rates ranging from 80% to 100% and is able to function properly with estimation error of up to 20%.

Dynamic target searching and tracking with swarm robots based on stigmergy mechanism

A strategy based on stigmergy mechanism for swarm robots interaction is proposed in this paper. The stigmergy mechanism refers to an interactive method in which robots communicate indirectly through the pheromone left in the environment by robots. The proposed stigmergic strategy that uses a vectorial pheromone model is applied for swarm robots to search and track a dynamic target. Some simulations and experiments are implemented to observe the performance of the strategy. In these investigations, RFID tags which are very cheap and convenient are arranged in the environment as carriers of the pheromones for indirect communication among robots. Robots read and write the vectorial pheromones while moving in the environment, and determine their behaviors through the vectorial pheromones. The results from simulations and experiments of using different numbers of robots show the swarm robots are able to find a dynamic target quickly and then keep a close track to the target, which demonstrates the feasibility and scalability of this strategy.

Analysis and optimization based on a sex pheromone and pesticide pest model with gestation delay

Sex pheromone, aiming at mating disruption (MD), being species specific and leaving no toxic residues in the produce grown, offers an attractive alternative to conventional pesticides. In this paper, by incorporating the gestation delay and sex pheromone, we explore the impact of MD control on the dynamic behaviors of pest system. Firstly, the boundness, stability and bifurcation of system are deliberated. Secondly, an optimal control problem based on sex pheromone and pesticide is transformed into an equivalent optimal parameter selection problem by introducing the constrain violation function. Additionally, the gradients of the cost function with respect to the dose of sex pheromone and the killing rate are given. Furthermore, simulations are executed to validate the validity of our method. Meanwhile, our results indicate that gestation delay increases the extinction risk of the population and liberating sex pheromone destroys the stability of equilibrium states.

Extended pheromone-based short-term traffic forecasting models for vehicular systems

An accurate short-term traffic forecasting model serves as an integral part to enhance the efficiency of vehicle rerouting and traffic light control strategies. The information exchange (pheromone) behavior of ants has been applied to forecast traffic conditions in existing pheromone models. These models were developed to forecast congestion on roads with signalized intersections by considering only green and red phases. Motivated by this issue, three short-term traffic forecasting models are proposed: (i) Extended Pheromone Model (EPM), (ii) Extended Pheromone Model with epsilon-Support Vector Regression (εSVR-EPM), and (iii) Extended Pheromone Model with Artificial Neural Network and Particle Swarm Optimization (ANNPSO-EPM). It is worth noticing that EPM is an algorithmic model whereas the other two are machine learning models. In all proposed models, a new color pheromone concept is proposed with two significant contributions. First, the color pheromone concept is developed to capture stochastic traffic conditions on the roads with non-signalized intersections. Second, the proposed concept is further extended to include all three color phases (red, yellow and green) to forecast dynamic changing traffic behaviors for roads with signalized intersections. The proposed color pheromone concept in EPM, εSVR-EPM, and ANNPSO-EPM is different from the existing models as it dynamically switches its computation techniques based on traffic light phases. All three proposed models can be realized through a Pheromone-based Multi-Agent System composed of Vehicle Agents and Intersection Agents coordinating locally with one another To promote practicality, Singapore City Hall map is employed in a microscopic simulator of Simulation of Urban Mobility (SUMO), showing that all proposed models outperform the other existing pheromone models.

Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System

Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was L max = 3 , the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and L max was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and L max to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks.

페르몬 모형을 활용한 옴니채널 쇼핑몰의 미래 시장예측에 관한 연구

페르몬 모형을 활용한 옴니채널 쇼핑몰의 미래 시장예측에 관한 연구

Nasal mucus glutathione transferase activity and impact on olfactory perception and neonatal behavior

In olfaction, to preserve the sensitivity of the response, the bioavailability of odor molecules is under the control of odorant-metabolizing enzymes (OMEs) expressed in the olfactory neuroepithelium. Although this enzymatic regulation has been shown to be involved in olfactory receptor activation and perceptual responses, it remains widely underestimated in vertebrates. In particular, the possible activity of OMEs in the nasal mucus, i.e. the aqueous layer that lined the nasal epithelium and forms the interface for airborne odorants to reach the olfactory sensory neurons, is poorly known. Here, we used the well-described model of the mammary pheromone (MP) and behavioral response in rabbit neonates to challenge the function of nasal mucus metabolism in an unprecedented way. First, we showed, in the olfactory epithelium, a rapid glutathione transferase activity toward the MP by ex vivo real-time mass spectrometry (PTR-MS) which supported an activity in the closest vicinity of both the odorants and olfactory receptors. Indeed and second, both the presence and activity of glutathione transferases were evidenced in the nasal mucus of neonates using proteomic and HPLC analysis respectively. Finally, we strikingly demonstrated that the deregulation of the MP metabolism by in vivo mucus washing modulates the newborn rabbit behavioral responsiveness to the MP. This is a step forward in the demonstration of the critical function of OMEs especially in the mucus, which is at the nasal front line of interaction with odorants and potentially subjected to physiopathological changes.

Pheromone Model Selection in Ant Colony Optimization for the Travelling Salesman Problem

As a meta-heuristic approach, Ant colony optimization (ACO) has many applications. In the algorithm selection of pheromone models is the top priority. Selecting pheromone models that don't suffer negative biases is a natural choice. Specifically for the travelling salesman problem, the first order pheromone is widely recognized. When come across travelling salesman problem, we study the reasons for the success of ant colony optimization from the perspective of pheromone models, and unify different order pheromone models. In tests, we have introduced the concept of sample locations and the similarity coefficient to pheromone models. The first order pheromone model and the second order pheromone model are compared and are further analysed. We illustrate that the second order pheromone model has better global search ability and diversity of population than the former. With appropriate-scale travelling salesman problems, the second order model performs better than the first order pheromone model.

Symmetry breaking on density in escaping ants: experiment and alarm pheromone model.

The symmetry breaking observed in nature is fascinating. This symmetry breaking is observed in both human crowds and ant colonies. In such cases, when escaping from a closed space with two symmetrically located exits, one exit is used more often than the other. Group size and density have been reported as having no significant impact on symmetry breaking, and the alignment rule has been used to model symmetry breaking. Density usually plays important roles in collective behavior. However, density is not well-studied in symmetry breaking, which forms the major basis of this paper. The experiment described in this paper on an ant colony displays an increase then decrease of symmetry breaking versus ant density. This result suggests that a Vicsek-like model with an alignment rule may not be the correct model for escaping ants. Based on biological facts that ants use pheromones to communicate, rather than seeing how other individuals move, we propose a simple yet effective alarm pheromone model. The model results agree well with the experimental outcomes. As a measure, this paper redefines symmetry breaking as the collective asymmetry by deducing the random fluctuations. This research indicates that ants deposit and respond to the alarm pheromone, and the accumulation of this biased information sharing leads to symmetry breaking, which suggests true fundamental rules of collective escape behavior in ants.

Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships

This paper proposes a swarm intelligence approach based on a disjunctive graph model in order to schedule a manufacturing system with resource flexibility and separable setup times. Resource flexibility assigns each operation to one of the alternative resources (assigning sub-problem) and, consequently, arranges the operation in the right sequence of the assigned resource (sequencing sub-problem) in order to minimize the makespan. Resource flexibility is mandatory for rescheduling a manufacturing system after unforeseen events which modify resource availability. The proposed method considers parallel (related) machines and enforces in a single step both the assigning and sequencing sub-problems. A neighboring function on the disjunctive graph is enhanced by means of a reinforced relation-learning model of pheromone involving more effective machine-sequence constraints and a dynamic visibility function. It also considers the overlap between the jobs feeding and the machine (anticipatory) setup times. It involves separable sequence-independent and dependent setup phases. The algorithm performance is evaluated by modifying the well-known benchmark problems for job shop scheduling. Comparison with other systems and lower bounds of benchmark problems has been performed. Statistical tests highlight how the approach is very promising. The performance achieved when the system addresses the complete problem is quite close to that obtained in the case of the classical job-shop problem. This fact makes the system effective in coping with the exponential complexity especially for sequence dependent setup times.