Results 91 to 100 of about 14,171 (244)

No evidence of a decoy effect in bees: Rewardless flowers do not increase bumblebees' preference for neighbouring flowers

open access: yesEcological Entomology, EarlyView.
Many plants retain nectarless flowers; we tested whether these act as “decoys” for bees by making neighbouring rewarding flowers seem more valuable—a cognitive bias known as the decoy effect. The presence of decoy flowers did not shift bumblebee preferences between two equally rewarding inflorescences, and bees quickly learned to avoid these nectarless
Mélissa Armand   +3 more
wiley   +1 more source

Artificial Bee Colony Training of Neural Networks [PDF]

open access: yes, 2014
The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks. This paper explores more carefully the performance of the ABC algorithm for optimizing the connection weights of feed-forward neural networks for classification tasks ...
John A. Bullinaria, Khulood AlYahya
openaire   +1 more source

The adaptive significance of sensory bias in a foraging context: floral colour preferences in the bumblebee Bombus terrestris [PDF]

open access: yes, 2007
Innate sensory biases could play an important role in helping naïve animals to find food. As inexperienced bees are known to have strong innate colour biases we investigated whether bumblebee (Bombus terrestris) colonies with stronger biases for the most
Lars Chittka   +6 more
core   +1 more source

Urban peatlands can harbour diverse insect communities but depend on appropriate habitat management

open access: yesInsect Conservation and Diversity, EarlyView.
Urban peatlands can support high insect diversity, including endangered species and peat bog specialists. Flower diversity boosts endangered species, while succession cover reduces overall insect richness—but bog condition and urbanization showed no major effects.
Nadja Pernat   +3 more
wiley   +1 more source

An artificial bee colony algorithm for the set covering problem

open access: yes, 2014
In this paper, we present a new Artificial Bee Colony algorithm to solve the non-unicost Set Covering Problem. The Artificial Bee Colony algorithm is a recent metaheuristic technique based on the intelligent foraging behavior of honey bee swarm ...
Cuesta R.   +3 more
core   +1 more source

From biology to biotechnology: Host‐regulation factors from parasitoid wasps are a source of bioactive molecules with translational potential

open access: yesInsect Molecular Biology, EarlyView.
Parasitoid wasps deploy maternal and embryonic factors to reprogramme host physiology. Venom, calyx fluid, polydnaviruses, teratocytes and larval secretions act in a coordinated, compartmentalised manner. Host‐regulation factors are promising sources of insecticidal, antimicrobial and bioinspired translational molecules.
Ciro Pedro G. Pinto   +2 more
wiley   +1 more source

Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset

open access: yesApplied Sciences
In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science.
Hadi Najafi Mohsenabad, Mehmet Ali Tut
doaj   +1 more source

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

open access: yesPolish Maritime Research, 2017
The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem.
Li Jianjun, Zhang Ru Bo
doaj   +1 more source

A Web-Service for Automated Software Refactoring Using Artificial Bee Colony Optimization

open access: yes, 2012
Automated software refactoring is one of the hard combinatorial optimization problems of search-based software engineering domain. The idea is to enhance the quality of the existing software under the guidance of software quality metrics through ...
Ersoy, Nur   +3 more
core  

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

open access: yes, 2019
Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures.
Sibel Arslan   +3 more
core   +1 more source

Home - About - Disclaimer - Privacy