scholarly journals Bees do not use nearest-neighbour rules for optimization of multi-location routes

2011 ◽  
Vol 8 (1) ◽  
pp. 13-16 ◽  
Author(s):  
Mathieu Lihoreau ◽  
Lars Chittka ◽  
Steven C. Le Comber ◽  
Nigel E. Raine

Animals collecting patchily distributed resources are faced with complex multi-location routing problems. Rather than comparing all possible routes, they often find reasonably short solutions by simply moving to the nearest unvisited resources when foraging. Here, we report the travel optimization performance of bumble-bees ( Bombus terrestris ) foraging in a flight cage containing six artificial flowers arranged such that movements between nearest-neighbour locations would lead to a long suboptimal route. After extensive training (80 foraging bouts and at least 640 flower visits), bees reduced their flight distances and prioritized shortest possible routes, while almost never following nearest-neighbour solutions. We discuss possible strategies used during the establishment of stable multi-location routes (or traplines), and how these could allow bees and other animals to solve complex routing problems through experience, without necessarily requiring a sophisticated cognitive representation of space.

Author(s):  
Hu Qin ◽  
Xinxin Su ◽  
Teng Ren ◽  
Zhixing Luo

AbstractOver the past decade, electric vehicles (EVs) have been considered in a growing number of models and methods for vehicle routing problems (VRPs). This study presents a comprehensive survey of EV routing problems and their many variants. We only consider the problems in which each vehicle may visit multiple vertices and be recharged during the trip. The related literature can be roughly divided into nine classes: Electric traveling salesman problem, green VRP, electric VRP, mixed electric VRP, electric location routing problem, hybrid electric VRP, electric dial-a-ride problem, electric two-echelon VRP, and electric pickup and delivery problem. For each of these nine classes, we focus on reviewing the settings of problem variants and the algorithms used to obtain their solutions.


Author(s):  
Eliana Mirledy Toro Ocampo ◽  
Frederico G. Guimarães ◽  
Ramón Alfonso Gallego Rendón

2019 ◽  
Vol 259 ◽  
pp. 1-18
Author(s):  
Julián Aráoz ◽  
Elena Fernández ◽  
Salvador Rueda

2017 ◽  
Vol 284 (1865) ◽  
pp. 20171097 ◽  
Author(s):  
Géraud de Premorel ◽  
Martin Giurfa ◽  
Christine Andraud ◽  
Doris Gomez

Iridescence—change of colour with changes in the angle of view or of illumination—is widespread in the living world, but its functions remain poorly understood. The presence of iridescence has been suggested in flowers where diffraction gratings generate iridescent colours. Such colours have been suggested to serve plant–pollinator communication. Here we tested whether a higher iridescence relative to corolla pigmentation would facilitate discrimination, learning and retention of iridescent visual targets. We conditioned bumblebees ( Bombus terrestris ) to discriminate iridescent from non-iridescent artificial flowers and we varied iridescence detectability by varying target iridescent relative to pigment optical effect. We show that bees rewarded on targets with higher iridescent relative to pigment effect required fewer choices to complete learning, showed faster generalization to novel targets exhibiting the same iridescence-to-pigment level and had better long-term memory retention. Along with optical measurements, behavioural results thus demonstrate that bees can learn iridescence-related cues as bona fide signals for flower reward. They also suggest that floral advertising may be shaped by competition between iridescence and corolla pigmentation, a fact that has important evolutionary implications for pollinators. Optical measurements narrow down the type of cues that bees may have used for learning. Beyond pollinator–plant communication, our experiments help understanding how receivers influence the evolution of iridescence signals generated by gratings.


2002 ◽  
Vol 29 (10) ◽  
pp. 1393-1415 ◽  
Author(s):  
Tai-Hsi Wu ◽  
Chinyao Low ◽  
Jiunn-Wei Bai

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