TSP Solving Utilizing Improved Ant Colony Algorithm

To solve the premature issue of TSP solving using the ant colony optimization algorithm (ACO), this paper proposes an improved ACO using particle swarm optimization (PSO) to solve the classic traveling salesman problem (TSP). The algorithm’s strategy includes three stages: firstly, establishing a mathematical model according to the optimization objective, and then solving the optimal path obtained by the particle swarm optimization algorithm. Finally, the pheromone concentration of this path in the ant colony mathematical model is enhanced according to the particle swarm optimization algorithm’s optimal path. A classic TSP case is used to compare the PSO and ACO. The results show that the proposed improved algorithm has a faster convergence speed and can converge to the optimal global solution, and its performance is better than that of ACO and PSO.

Path Optimization of Joint Delivery Mode of Trucks and UAVs

With the development of e-commerce and information technology, new modes of distribution are emerging. A new type of distribution tool, UAV (unmanned aerial vehicle), has entered into the public’s field of vision. In the background of growing e-commerce, this paper proposes a new delivery mode of joint delivery of trucks and UAVs which particularly has been popular in recent years, with the advantages of prompt delivery, low cost, and independence from terrain restrictions, while traditional transportation tools such as trucks have more advantages in terms of flight distance and load capacity. Therefore, the joint delivery mode of trucks and UAVs proposed in this paper can well realize the complementary advantages of trucks and UAVs in the distribution process and consequently optimize the distribution process. Moreover, the growing e-commerce promotes customers’ higher needs for delivery efficiency and the integrity of the delivered goods which urges companies to pay more attention to customers’ satisfaction. This paper analyzes the joint delivery mode of trucks and UAVs, aims to minimize total delivery cost and maximize customer satisfaction, and builds a multiobjective optimization model for joint delivery. Furthermore, an improved ant colony algorithm is proposed in order to solve the mode in this paper. In order to effectively avoid prematurity of the ant colony algorithm, the limited pheromone concentration and the classification idea of the artificial bee colony algorithm are introduced to improve the ant colony algorithm. Finally, some experiments are simulated by MATLAB software, and the comparison shows that the joint delivery of trucks and UAVs has more advantages, and the improved ant colony algorithm is more efficient than the traditional ant colony.

Honeybee communication during collective defence is shaped by predation

Background Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defence. To do so, honeybees use an alarm pheromone that recruits nearby bees into mass stinging of the perceived threat. This alarm pheromone is carried directly on the stinger; hence, its concentration builds up during the course of the attack. We investigate how bees react to different alarm pheromone concentrations and how this evolved response pattern leads to better coordination at the group level. Results We first present a dose-response curve to the alarm pheromone, obtained experimentally. This data reveals two phases in the bees’ response: initially, bees become more likely to sting as the alarm pheromone concentration increases, but aggressiveness drops back when very high concentrations are reached. Second, we apply Projective Simulation to model each bee as an artificial learning agent that relies on the pheromone concentration to decide whether to sting or not. Individuals are rewarded based on the collective performance, thus emulating natural selection in these complex societies. By also modelling predators in a detailed way, we are able to identify the main selection pressures that shaped the response pattern observed experimentally. In particular, the likelihood to sting in the absence of alarm pheromone (starting point of the dose-response curve) is inversely related to the rate of false alarms, such that bees in environments with low predator density are less likely to waste efforts responding to irrelevant stimuli. This is compensated for by a steep increase in aggressiveness when the alarm pheromone concentration starts rising. The later decay in aggressiveness may be explained as a curbing mechanism preventing worker loss. Conclusions Our work provides a detailed understanding of alarm pheromone responses in honeybees and sheds light on the selection pressures that brought them about. In addition, it establishes our approach as a powerful tool to explore how selection based on a collective outcome shapes individual responses, which remains a challenging issue in the field of evolutionary biology.

Optimal search and rescue route design using an improved ant colony optimization

In this paper, an improved ant colony algorithm is proposed for the route design of maritime emergency search and rescue. To solve the problem that the ant colony algorithm is easy to fall into local optimal solutions in the process of searching, the pheromone concentration updating strategy of the original ant colony algorithm is provided. According to the actual situation of maritime search and rescue, the path weight based on the time of falling into the water is introduced into the algorithm to obtain the optimal route. The simulation results show that the improved algorithm can be used for route design, and obtain the optimal route suitable for sea search and rescue.

Airborne Pheromone Quantification in Treated Vineyards with Different Mating Disruption Dispensers against Lobesia botrana

Mating disruption (MD) is widely used against the European grapevine moth (EGVM), Lobesia botrana (Denis and Schiffermüller; Lepidoptera: Tortricidae), by installing passive dispensers or aerosol devices. The present work reports a new sampling and quantification methodology to obtain absolute data about field airborne pheromone concentration based on air samplings and sensitive chromatographic-spectroscopic methods. Samplings were performed in fields treated with passive dispensers or aerosol devices at different moments throughout the crop cycle to study how they act and how the disruption is triggered. Moreover, pheromone adsorption and releasing capacity of vine leaves were studied to elucidate their role in the disruption. Although both types of dispensers were effective in limiting the damage inflicted by EGVM, they performed differently and provided different airborne pheromone concentration profiles. Results also proved that leaves were able to adsorb and release part of the airborne pheromone acting as subsequent and additional pheromone sources. This fact could explain the different concentration profiles. Moreover, our results suggest that lower pheromone emission than that of the current passive dispensers still could provide an adequate performance in the field. Competitive mechanisms involved in MD using both dispensers, the dynamics of the airborne pheromone throughout the time and the importance of the canopy are discussed.

Research on path planning based on new fusion algorithm for autonomous vehicle

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.

Efficacy of communication disruption of Thaumetopoea pityocampa (Lepidoptera: Thaumetopoeidae) with low pheromone formulation

SummaryMating disruption (MD) has been a successful approach for pest control of several lepidoptera. Field trials to evaluate the efficacy of communication disruption of low pheromone load formulation on Thaumetopoea pityocampa were carried out in 2010 and 2011 in an urban park. The efficacy of MD was assessed by comparing male T. pityocampa catches in pheromone traps, between MD and Control areas. In the 1st year of the application the percentage of male inhibition ranged from 85 to 100% during the 1st month of the flight period and 95-100% during the whole flight period in the 2nd year. The pheromone remained in the polymeric matrix was almost 30% after 7 weeks under laboratory aging conditions. Combining the pheromone release results with the male disorientation results we can assume that after 7 weeks the remaining pheromone concentration was still sufficient to achieve MD. This study indicates that air permeation with the major sex pheromone component (Z)-13-hexadecen-11-ynyl acetate, at a rate of 20 g/ha for one application per season, can affect the orientation of T. pityocampa males. Since mating disruption is an environmentally safe method for pest control, it could be a valuable tool to control T. pityocampa in urban areas and parks.

Sex-specific molecular specialization and activity rhythm dependent gene expression changes in honey bee antennae

ABSTRACTEusocial insects, like honey bees, which show an elaborate division of labor involving morphologically and physiologically specialized phenotypes provide a unique toolkit to study molecular underpinnings of behavior as well as neural processing. In this study, we performed an extensive RNA-seq based comparison of gene expression levels in the antennae of honey bee drones and foragers collected at different time of days and activity states to identify molecules involved in peripheral olfactory processing and provide insights into distinct strategies in sensory processing. First, honey bee drone and worker antennae differ in the number of olfactory receptor genes (ORs) showing a biased expression pattern. Only 19 Ors were higher expressed in drone antennae, whereas 54 Ors were higher expressed in workers. Second, drone antennae showed predominant higher expression of genes involved in energy metabolism, and worker antennae showed a higher expression of genes involved in neuronal communication. Third, drones and afternoon-trained foragers showed similar daily changes in the expression of major clock genes, per and cry2. Most of the other genes showing changes with the onset of daily activity were specific to drones and foragers suggesting sex-specific circadian changes in antennae. Drone antennae are specialized to detect small amounts of queen’s pheromone and quickly respond to changes in pheromone concentration involving energetically costly action potentials, whereas forager antennae are predominantly involved in behavioral context dependent detection and discrimination of complex odor mixtures which requires mechanisms of sensory filtering and neural plasticity.