Foraging Behavior Of Honey Bees Research Articles

Seed treatment with neonicotinoid insecticides does not affect the foraging behavior of honey bees

Seed treatment with neonicotinoid insecticides does not affect the foraging behavior of honey bees

Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm

Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in both forward and backward sides. Furthermore, the BA was used to optimize the learning rate factor in the fully connected layer. In this study, artificial porosity images were used for testing the algorithms; since the input data were images, a Convolutional Neural Network (CNN) was added in order to extract the features in the images to feed into the LSTM for predicting the percentage of porosity in the sequential layers of artificial porosity images that mimic real CT scan images of products manufactured by the Selective Laser Melting (SLM) process. Applying a Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) yielded a porosity prediction accuracy of 93.17%. Although using Bayesian Optimization (BO) to optimize the LSTM parameters mentioned previously did not improve the performance of the LSTM, as the prediction accuracy was 93%, adding the BA to optimize the same LSTM parameters did improve its performance in predicting the porosity, with an accuracy of 95.17% where a hybrid Bees Algorithm Convolutional Neural Network Long Short-Term Memory (BA-CNN-LSTM) was used. Furthermore, the hybrid BA-CNN-LSTM algorithm was capable of dealing with classification problems as well. This was shown by applying it to Electrocardiogram (ECG) benchmark images, which improved the test set classification accuracy, which was 92.50% for the CNN-LSTM algorithm and 95% for both the BO-CNN-LSTM and BA-CNN-LSTM algorithms. In addition, the turbofan engine degradation simulation numerical dataset was used to predict the Remaining Useful Life (RUL) of the engines using the LSTM network. A CNN was not needed in this case, as there was no feature extraction for the images. However, adding the BA to optimize the LSTM parameters improved the prediction accuracy in the testing set for the LSTM and BO-LSTM, which increased from 74% to 77% for the hybrid BA-LSTM algorithm.

Hidrolik Silindir Tasarımı Ve Optimizasyonunda Arı Algoritmasının Uygulanması

In this study, mass minimization of a simple double-acting hydraulic cylinder has been studied using The Bees Algorithm (BA) for a specific force and known material, considering the buckling and pressure constraints. A Hydraulic cylinder is a hydraulic actuator that creates linear movement by converting hydraulic energy back to a mechanical movement. Hydraulic-driven working machines are widespread in the industry today. Hydraulic cylinders are used in mobile applications such as container lifting devices, excavators, dump trucks, loaders, graders and dozers. Weight reduction in these cylinders plays a fundamental role in the performance of the machine in terms of lifting capacity, speed, costing, etc. The Bees Algorithm is a metaheuristic algorithm that mimics the natural foraging behavior of honey bees to find the optimum solutions. The advantages over other algorithms are its ability to search both locally and globally and being applicable for several optimization problems with the chance to be integrated with other algorithms. In this study, it is also aimed to determine the optimal parameters of the bees algorithm for minimum computation cost.

Solving the Travelling Salesman Problem by Using Artificial Bee Colony Algorithm

Travelling Salesman Problem (TSP) is a list of cities that must visit all cities that start and end in the same city to find the minimum cost of time or distance. The Artificial Bee Colony (ABC) algorithm was used in this study to resolve the TSP. ABC algorithms is an optimisation technique that simulates the foraging behaviour of honey bees and has been successfully applied to various practical issues. ABC algorithm has three types of bees that are used by bees, onlooker bees, and scout bees. In Bavaria from the Library of Traveling Salesman Problem, the distance from one city to another has been used to find the best solution for the shortest distance. The result shows that the best solution for the shortest distance that travellers have to travel in all the 29 cities in Bavaria is 3974km.

A room-oriented artificial bee colony algorithm for optimizing the patient admission scheduling problem

Patient admission scheduling (PAS) is a tasking combinatorial optimization problem where a set of patients is assigned to limited facilities such as rooms, timeslots, and beds subject to satisfying a set of predefined constraints. The investigations into the performance of population-based algorithms that utilized to tackle the PAS problem considered in this paper reveal their weaknesses in obtaining quality solutions that create a space to investigate the performance of another population-based method. Thus, in this paper, an Artificial Bee Colony Algorithm (ABC) is proposed to tackle the formulation of the PAS problem under consideration. It is a class of swarm intelligence metaheuristic algorithms based on the intelligent foraging behaviour of honey bees developed to solve continuous and complex optimization problems. Due to the discretization of the PAS, the continuous nature of the ABC algorithm is changed to cope with the rugged solution space of the PAS. The initial feasible solution to the PAS problem is obtained using the room-oriented approach. Then the ABC algorithm optimizes the feasible solutions with the aid of three neighbourhood structures embedded within the employed bee and the onlooker bee operators of the algorithm. The performance of the proposed ABC algorithm based on three different parameters, the solution number (SN), limit value (LV), and the maximum cycle number (MCN) is evaluated on six standard benchmark datasets of the PAS. Two of these main parameters (i.e. SN and LV) are fine-tuned to obtain the best solutions on instances like Test-data 1 = 679.80, Test-data 2 = 1180.40, Test-data 3 = 787.40, Test-data 4 = 1198.60, Test-data 5 = 636.80, and Test-data 6 = 818.60. The best solutions obtained by the proposed method are evaluated against the results of the 19 comparative algorithms comprising five population-based methods, eleven heuristic, and hyperheuristic-based methods, and three integer programming-based methods. The proposed method shows its supremacy in the performance by achieving the best results in all the instances of the dataset when compared with five population-based methods (DFPA, HSA, MBBO-GBS, BBO-GBS, and BBO-RBS) and producing the best results in five instances when compared with eleven heuristic and hyperheuristic-based methods (LAHC, DHS-GD, HTS, DHS-SA, ADAPTIVE GD, GD, HH-GD, DHS-IO, HH-SA, HH-IE, TA) and Finally, it had a competitive performance with the other three Integer programming methods (MIP warm start, MIP-Heuristic, CG) that worked on the same formulations of the PAS. In a nutshell, the proposed ABC algorithm could be adopted as a new template algorithm for the PAS community.

Load Balancing in Cloud Computing Based on Honey Bee Foraging Behavior and Load Balance Min-Min Scheduling Algorithm

Cloud computing relies on the collection and distribution of services from internet-based data centers. With the large resource pool available in internet wide range of users are accessing the cloud. Load balance is important feature involving resource allocation to prevent overloading of any system or optimal use of resources. Major load in cloud network are concerned with CPU, memory and network. This cloud computing aspect has not yet earned too much coverage. Although load balancing is an important feature for cloud computing, concurrent computing etc. In these areas, several algorithms were suggested to solve load balance problem. However, it does recommend very few cloud computing algorithms. Given that cloud storage differs considerably from all other environments, particular load balancing algorithm should will built in sort to serve its needs. This work proposes novel load-balancing algorithm based on artificial bee colony algorithm and load balancing min-min scheduling algorithm for balancing load in cloud computing network. Simulation here is carried out in clouds to generate comparative results. Improving on various parameters like power consumption, resource utilization, stability of system are some major areas focused on. This work has used algorithm that has the best efficiency of resources, optimal performance, minimal response time, scalability and durability in integrated resource planning.

Supplemental feeds and foraged corn grain dust: a comparison of the number of days survived in vitro by young adult honey bees (Apis mellifera)

To investigate the value of atypical foraging behavior of honey bees (Apis mellifera) during dearth, we compared the number of days survived in vitro among young adult honey bees kept in hoarding cages using various feeding strategies. Treatment groups were fed solid patty diets consisting of 100%, 67%, or 33% bee gathered corn grain dust, 100%, 67%, or 33% hand collected corn grain dust, or 100% commercially available pollen substitute. The bees were also provided either 0.5:1 sucrose syrup, 1.5:1 sucrose syrup, or 1.5:1 sucrose syrup in concert with the aforementioned supplements. No significant difference was observed in the number of days survived among groups receiving different types of solid patty feed, demonstrating that atypical feed gathered during dearth, corn grain dust in this instance, may provide sufficient nutrition for survival when preferable forage is unavailable. There was a statistically significant difference in the number of days survived in vitro among groups receiving different syrup types, indicating a potential survival benefit when supplement is included in syrup provided by beekeepers during dearth.

Sizing and layout optimization of truss structures with artificial bee colony algorithm

In this paper, a swarm intelligence-based optimization technique called Artificial Bee Colony algorithm (ABC) is employed in combined optimization of truss structures. The objective is to optimize the layout and members size of truss structures with displacement, stress and buckling constraints. The ABC is based on simulating the intelligent foraging behavior of honey bees. The nodal coordinates of the joints and the cross-sectional areas of the members for the truss structure system are the design variables of shape and size optimization, respectively. Allowable stress, Euler buckling stress, and displacement are considered as the problem constraints. The efficiency of the ABC is tested in four benchmark structural optimization problems. The results clearly reveal the superiority of ABC over other algorithms in terms of optimized weight, standard deviation and number of structural analyses. The ABC demonstrates a robust performance with a 100% success rate.

Absolute versus stochastic stability of the artificial bee colony in synchronous and sequential modes

The artificial bee colony (ABC) is a population-based optimization algorithm that mimics the foraging behavior of honeybees. Here, we focus on the parameter setting that ensures the ABC algorithm stability. Therefore, this paper introduces a matrix-iterative model, taking into account the coupling within bees. Moreover, the model considered the difference between update modes, i.e., synchronous or sequential. The necessary conditions for absolute stability were derived under the quasi-deterministic assumption. We further investigated the criteria for first and second-order stochastic stability. These criteria report on the ranges for setting the uniform distributions of the ABC algorithm. Finally, some supporting simulations were carried out on CEC 2017 benchmark functions in different search space dimensions (10, 30, and 50) and on twenty real-world problems (CEC 2011). Six considered ABC variants tested the derived and state-of-the-art criteria in different update modes. Moreover, ABC algorithms were compared with some state-of-the-art metaheuristics (Particle Swarm Optimization, Gravitational Search Algorithm, and Grey Wolf Optimizer). The overall results show that stochastic stability proffers ABC competitiveness. The two update modes may alter the ABC performance only slightly or drastically, depending on the problem, with no evidence on the supremacy of one of them.

An Enhanced Discrete Bees Algorithm for Resource Constrained Optimization Problems

In this paper, we propose a novel efficient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of project scheduling problems and reduces the number of parameters to be tuned. The proposed EDBA is tested on well-known benchmark problem instance sets from Project Scheduling Problem Library (PSPLIB) and compared with other approaches from the literature. The promising computational results reveal the effectiveness of the proposed approach for solving the RCPSP problems of various scales.

Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation

Artificial bee colony (ABC) algorithm is an efficient biological-inspired optimization method, which mimics the foraging behavior of honey bees to solve the complex and nonlinear optimization problems. However, in some cases, it suffers from inefficient exploration, low exploitation and slow convergence rate. These shortcomings cause the problem of stagnation at local optimum which is dangerous in determining the true solution (optima) of the problem. Therefore, in the present paper, an attempt has been made toward the removal of the drawbacks from the classical ABC by proposing a novel hybrid method called SCABC algorithm. The SCABC algorithm hybridizes the ABC with sine cosine algorithm (SCA) to upgrade the level of exploitation and exploration in the classical ABC algorithm. The SCA is a recently introduced algorithm, which uses the trigonometric functions sine and cosine to perform the search. The validation of the SCABC algorithm is performed on a well-known benchmark set of 23 optimization problems. The various analysis metrics such as statistical, convergence and performance index analysis verify the better search ability of the SCABC as compared to classical ABC, SCA. The comparison with some other optimization algorithms demonstrates a comparatively better state of exploitation and exploration in the SCABC algorithm. Moreover, the SCABC is also employed on multilevel thresholding problems. The various performance measures demonstrate the efficacy of the SCABC algorithm in determining the optimal thresholds of gray images.

An individual dependent multi-colony artificial bee colony algorithm

Artificial bee colony (ABC) is a well-known swarm intelligence based algorithm that simulates the foraging behavior of honey bees for food sources. However, the basic ABC only evolves one colony and both the employed bee phase and onlooker phase utilize the same solution search equation, which performs well in exploration but poorly in exploitation. Inspired by the good working efficiency of labor division and the coordination of complementary elements, we propose a novel individual dependent multi-colony ABC algorithm, abbreviated as IDABC, in which the whole colony is divided into three sub-colonies, i.e., inferior sub-colony, mid sub-colony and superior sub-colony, based on the fitness function values of the individuals involved. Three evolution operators with different searching biases are introduced into the corresponding sub-colonies in order to play different roles. Furthermore, we improve the related evolution operators by incorporating the fitness and distance information of individuals and the control parameters involved in the food source perturbation that are dynamically adjusted according to the search experience. And an orthogonal learning mechanism is suggested for scout bee searching so as to generate potential solutions. The proposed IDABC is examined in a set of benchmark instances taken from the CEC2013 and CEC2014 competition, and comparative results demonstrate the competitive performance of IDABC.

Foraging and homing behavior of honey bees (Apis mellifera) during a total solar eclipse.

Exceptional natural phenomena, such as those that occur during a total solar eclipse, provide unique opportunities to study animal behavior outside the naturally evolved context, which can be informative in more general terms. Circumstantial descriptions of abnormal animal behavior during solar eclipses abound, although scientific studies conducted during an eclipse are relatively rare due to inherent logistical difficulties. Here, honey bee foraging and homing behavior were studied during the total solar eclipse of August 21, 2017. In the first experiment, we studied foraging behavior of honey bees during the progression of the solar eclipse and found that the foraging activity drastically decreased but did not completely cease during the totality of the eclipse, in contrast to previous reports of complete cessation. The data indicate that the level of ambient light can largely overrule the internal circadian rhythm of foraging honey bees. Furthermore, colonies with a higher need for foraging decreased their foraging activity less than satiated colonies, consistent with the hypothesis that individual foraging decisions may be influenced by colony state, which affects cost-benefit analyses. In a second experiment, the temporal dynamics of homing of released workers and drones was compared in periods before, during, and after the solar eclipse. During the totality of the eclipse, very few bees arrived back at their hive, while homing before the total eclipse was accelerated, particularly in drones. The results suggest that, while the homing abilities of honey bees are not compromised until the sun is completely eclipsed, they may still interpret the diminishing light as an indicator of deteriorating flight conditions. Our unique study provides some insight into the control of honey bee foraging behavior when external cues and internal circadian rhythms are at odds, lent support to the notion that food deprivation can lead to riskier foraging, and indicated that homing in honey bees is possible even with very small amounts of sunlight.

Larval pheromones act as colony-wide regulators of collective foraging behavior in honeybees

When animals move or forage in groups, collective behaviors arise from independent decisions that individuals make based on limited information about the environment. In decentralized systems in which individuals use local cues to decide how to allocate their time amongst multiple tasks, a “global” signal detectable over large distances by all members of the group could have a profound effect on task allocation and coordination. Honeybees provide a unique opportunity to study how information transfer modulates behavior because they produce pheromones that can regulate the actions of thousands of individuals in a colony. We used electrophysiological and behavioral assays to compare the transmission modes of 2 larval pheromones to test the hypothesis that larval pheromones can act as “global” signals by rapidly regulating behavior throughout a colony without direct physical interactions between individuals. By studying mechanisms of pheromone transmission at the individual and colony level, we provide evidence that larval pheromones act as direct rapid and powerful regulators of behavior, even among individuals too far away from each other to use visual or tactile cues. Therefore, our results suggest that in some cases, global signals can be important regulators of collective behavior.

Natural selection methods for artificial bee colony with new versions of onlooker bee

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms simulate the foraging behavior of honey bees in their hive. ABC starts with a colony of artificial bees with sole aim of discovering the place of food sources with high nectar amount using the solution search equation in the employed bee and onlooker bee operators. However, the solution search equation is good in exploration and poor in exploitation. In this paper, the solution search equation of the onlooker bee is modified by using a value of the fittest food sources selected by a set of selection schemes inspired from the evolutionary algorithms. This is to guide the search process of onlooker bee toward the fittest food sources from the population in order to empower the exploitation capability and convergence. Four selection schemes are incorporated with the ABC algorithm to choose the fittest food sources in four versions as follows: global-best, tournament, linear rank, and exponential rank. For evaluation purposes, 10 classical benchmark optimization functions are used to study the sensitivity analysis of each ABC algorithm to its parameters. The performance of the proposed ABC versions is compared with the original ABC version in order to study the effectiveness of the modifications. In addition, a comparative evaluation of ABC algorithms is carried out against the state-of-the-art methods that worked on CEC2005 benchmark functions, CEC2015 benchmark functions, and two real-world cases of economic load dispatch problem. The experimental results show that the selection schemes incorporated within the search equation of the onlooker bee directly affects the performance of ABC algorithm.

A Survey on Honey Bee Foraging Behavior and Its Improvised Load Balancing Technique

A Survey on Honey Bee Foraging Behavior and Its Improvised Load Balancing Technique

Artifical bee colony algorithm using problem-specific neighborhood strategies for the tree t-spanner problem

A tree t-spanner is a spanning tree T in which the ratio of distance between every pair of vertices is at most t times their shortest distance in a connected graph, where t is a value called stretch factor of T. On a given connected, undirected, and weighted graph, this paper studies the tree t-spanner problem (Tree_t-SP) that aims to find a spanning tree whose stretch factor is minimum amongst all spanning trees of the graph. Being a NP-Hard for any fixed t>1, this problem is under-studied in the domain of metaheuristic techniques. In literature, only genetic algorithm has been proposed for this problem. This paper presents an artificial bee colony (ABC) algorithm for this problem, where ABC algorithm is a swarm intelligence technique inspired by intelligent foraging behavior of honey bees. Neighborhood strategies of ABC algorithm particularly employ problem-specific knowledge that makes ABC algorithm highly effective in searching high quality solutions in less computational time. Computational experiments on a large set of randomly generated graph instances exhibit superior performance of ABC algorithm over the existing genetic algorithm for the Tree_t-SP.

Load balanced transaction scheduling using Honey Bee Optimization considering performability in on‐demand computing system

SummaryThe scheduling of load balanced transactions is an emerging problem in on‐demand computing system. This problem can be studied considering several parameters such as resource availability, performance, and reliability. The existing approaches to find the exact solutions for this problem are limited. This paper presents a Honey Bee Optimization (HBO)–based method to solve the mentioned problem. In this method, first, the load of the system is balanced and then the transactions are scheduled using foraging behavior of honey bees to find the optimal solutions. We also modify four known scheduling algorithms such as Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized to obtain transaction scheduling algorithms for the purpose of comparison with our proposed algorithm. The compared results show that the proposed algorithm performs better than the modified existing algorithms.

Shuffling cross–validation–bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography

ABSTRACTBee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross–validation–BA (CV–BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV–BA. The results showed that shuffling CV–BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV–BA–MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV–BA−SVM method were obtained as 0.0704 and 0.9922, respectively.

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.