Foraging Behavior Of Honey Bees Research Articles

Impact of Modification Rate in Artificial Bee Colony for Engineering Design Problems

Artificial Bee Colony (ABC), a recently proposed population based search heuristics which takes its inspiration from the intelligent foraging behavior of honey bees. In this study we have studied the impact of modification rate (MR) in basic ABC by gradually increasing it from 0.1 to 0.9. This impact is studied on four engineering design problems taken from literature. The simulated results show that it is beneficial to set the modification rate to a lower value.

Bees algorithm for interference suppression of linear antenna arrays by controlling the positions of selected elements

An efficient method based on bees algorithm (BA) for the pattern synthesis of linear antenna arrays with the prescribed nulls is presented. Nulling of the pattern is achieved by controlling only the positions of selected elements. The BA is an optimization algorithm inspired by the natural foraging behavior of honey bees to find the optimal solution. Simulation results for Chebyshev patterns with the imposed single and multiple nulls are given to show the effectiveness of the proposed method.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

Improving artificial bee colony with one-position inheritance mechanism

Artificial bee colony (ABC) algorithm simulates the foraging behavior of honey bees. It shows good performance in many application problems and large scale optimization problems. However, variation of a solution in the ABC algorithm is only employed on one dimension of the solution. This would sometimes hamper the convergence speed of the ABC algorithm, especially for large scale optimization. This paper proposes a one-position inheritance (OPI) mechanism to overcome this drawback. The OPI mechanism aims to promote information exchange amongst employed bees of the ABC algorithm. For separable function, OPIABC has a higher probability resulting in function value improvement of the worst positions than ABC. Through one-position information exchange, the OPI mechanism can assist the ABC algorithm to find promising solutions. This mechanism has been tested on a set of 25 test functions with \(D= 30\) and on CEC 2008 test suite with \(D= 100\) and 1,000. Experimental results show that the OPI mechanism can speed up the convergence of the ABC algorithm. After the use of OPI, the performance of the ABC algorithm is significantly improved for both rotated problems and large scale problems. OPIABC is also competitive on both test suites comparing with other recently proposed swarm intelligence metaheuristics (e.g. SaDE and PSO2011). Furthermore, the OPI mechanism can greatly enhance the performance of other improved ABC algorithms.

Synergistic effects of non- Apis bees and honey bees for pollination services

In diverse pollinator communities, interspecific interactions may modify the behaviour and increase the pollination effectiveness of individual species. Because agricultural production reliant on pollination is growing, improving pollination effectiveness could increase crop yield without any increase in agricultural intensity or area. In California almond, a crop highly dependent on honey bee pollination, we explored the foraging behaviour and pollination effectiveness of honey bees in orchards with simple (honey bee only) and diverse (non-Apis bees present) bee communities. In orchards with non-Apis bees, the foraging behaviour of honey bees changed and the pollination effectiveness of a single honey bee visit was greater than in orchards where non-Apis bees were absent. This change translated to a greater proportion of fruit set in these orchards. Our field experiments show that increased pollinator diversity can synergistically increase pollination service, through species interactions that alter the behaviour and resulting functional quality of a dominant pollinator species. These results of functional synergy between species were supported by an additional controlled cage experiment with Osmia lignaria and Apis mellifera. Our findings highlight a largely unexplored facilitative component of the benefit of biodiversity to ecosystem services, and represent a way to improve pollinator-dependent crop yields in a sustainable manner.

Artificial bee colony optimization for multi-area economic dispatch

This paper presents artificial bee colony optimization for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system.

A Dynamic Intelligent Decision Approach to Dependency Modeling of Project Tasks in Complex Engineering System Optimization

Complex engineering system optimization usually involves multiple projects or tasks. On the one hand, dependency modeling among projects or tasks highlights structures in systems and their environments which can help to understand the implications of connectivity on different aspects of system performance and also assist in designing, optimizing, and maintaining complex systems. On the other hand, multiple projects or tasks are either happening at the same time or scheduled into a sequence in order to use common resources. In this paper, we propose a dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. The approach takes this decision process as a two-stage decision-making problem. In the first stage, a task clustering approach based on modularization is proposed so as to find out a suitable decomposition scheme for a large-scale project. In the second stage, according to the decomposition result, a discrete artificial bee colony (ABC) algorithm inspired by the intelligent foraging behavior of honeybees is developed for the resource constrained multiproject scheduling problem. Finally, a certain case from an engineering design of a chemical processing system is utilized to help to understand the proposed approach.

Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles

This paper presents the optimization of key component sizes and control strategy for parallel hybrid electric vehicles (parallel HEVs) using the bees algorithm (BA). The BA is an intelligent optimization tool that mimics the food foraging behavior of honey bees. Parallel HEV configuration and electric assist control strategy were used to conduct the research. The values of the key component size and the control strategy parameters were adjusted according to the BA to minimize the weighted sum of fuel consumption (FC) and emissions, while the vehicle performance satisfies the PNGV constraints. In this research, the software ADVISOR was used as the simulation tool, and the driving cycles FTP, ECE-EUDC and UDDS were employed to evaluate FC, emission and dynamic performance. The results demonstrate that the BA is a powerful tool in parallel HEV optimization to determine the optimal parameters of component sizes and control strategy, resulting in the improvement of FC and emissions without sacrificing vehicle performance. In addition, the BA is able to define a global solution with a high rate of convergence.

Artificial Bee Colony and Its Application for Image Fusion

Artificial Bee Colony (ABC) is one of the latest swarm algorithm based on the intelligent foraging behavior of honey bees introduced in the year 2005 by Karaboga since then it has been used for optimization of various solutions. And it is recently introduced for processing and analysis of images such as segmentation, object recognition and image retrieval. Fusing images from a vast collection of different images has become one of the interesting challenges and has drawn the attention of researchers towards the development of fusion techniques. In this paper, we have proposed the usage of ABC for optimal fusion of multi-temporal images and studied the effect of variation in the source area.

Hybrid harmony search and artificial bee colony algorithm for global optimization problems

Harmony search (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. In order to enhance the accuracy and convergence rate of harmony search, a hybrid harmony search is proposed by incorporating the artificial bee colony algorithm (ABC). The artificial bee colony algorithm is a new swarm intelligence technique inspired by intelligent foraging behavior of honey bees. The ABC and its variants are used to improve harmony memory (HM). To compare and analyze the performance of our proposed hybrid algorithms, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the hybrid algorithms are discussed by a uniform design experiment. Numerical results show that the proposed algorithms can find better solutions when compared to HS and other heuristic algorithms and are powerful search algorithms for various global optimization problems.

Halton Based Initial Distribution in Artificial Bee Colony Algorithm and its Application in Software Effort Estimation

Artificial Bee Colony (ABC) is an optimization algorithm that simulates the foraging behavior of honey bees. It is a population based search technique whose performance depends largely on the distribution of initial population. Generally, uniform distributions are preferred since they best reflect the lack of knowledge about the optimum’s location. Moreover, these are easy to generate as most of the programming languages have an inbuilt function for generating uniformly distributed random numbers. However, in case of a population dependent optimization algorithm like that of ABC, random numbers having uniform probability distribution may not be a good choice as they may not be able exploit the search space fully. This paper uses quasi random numbers based on Halton sequence for the initial distribution and have compared the simulation results with initial population generated using uniform distribution. The proposed variant, termed as Halton based ABC (H-ABC), is validated on a set of 15 standard benchmark problems, 6 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and has been used for solving the real life problem of estimating the cost model parameters. Numerical results indicate the competence of the proposed algorithm.

Reservoir characterisation using artificial bee colony optimisation

To obtain an accurate estimation of reservoir performance, the reservoir should be properly characterised. One of the main stages of reservoir characterisation is the calibration of rock property distributions with flow performance observation, which is known as history matching. The history matching procedure consists of three distinct steps: parameterisation, regularisation and optimisation. In this study, a Bayesian framework and a pilot-point approach for regularisation and parameterisation are used. The major focus of this paper is optimisation, which plays a crucial role in the reliability and quality of history matching. Several optimisation methods have been studied for history matching, including genetic algorithm (GA), ant colony, particle swarm (PS), Gauss-Newton, Levenberg-Marquardt and Limited-memory, Broyden-Fletcher-Goldfarb-Shanno. One of the most recent optimisation algorithms used in different fields is artificial bee colony (ABC). In this study, the application of ABC in history matching is investigated for the first time. ABC is derived from the intelligent foraging behaviour of honey bees. A colony of honey bees is comprised of employed bees, onlookers and scouts. Employed bees look for food sources based on their knowledge, onlookers make decisions for foraging using employed bees’ observations, and scouts search for food randomly. To investigate the application of ABC in history matching, its results for two different synthetic cases are compared with the outcomes of three different optimisation methods: real-valued GA, simulated annealing (SA), and pre-conditioned steepest descent. In the first case, history matching using ABC afforded a better result than GA and SA. ABC reached a lower fitness value in a reasonable number of evaluations, which indicates the performance and execution-time capability of the method. ABC did not appear as efficient as PSD in the first case. In the second case, SA and PDS did not perform acceptably. GA achieved a better result in comparison to SA and PSD, but its results were not as superior as ABC’s. ABC is not concerned with the shape of the landscape; that is, whether it is smooth or rugged. Since there is no precise information about the landscape shape of the history matching function, it can be concluded that by using ABC, there is a high chance of providing high-quality history matching and reservoir characterisation.

Using the Bees Algorithm to select the optimal speed parameters for wind turbine generators

The Bees Algorithm is a recently developed optimization technique that mimics the foraging behavior of honey bees in nature. This study investigates the use of the Bees Algorithm for the selection of the optimal operating speed parameters for wind power units. Three speed parameters need to be optimized, namely, the rated, cut-in, and cut-off (furling) speed of the turbine. The aim of the optimization process is to maximize the yearly power yield and turbine usage time. The choice of the best parameters depends from the wind frequency distribution at the site of installation. Eleven locations on the coastal areas of Egypt were chosen as case studies. The well-known Particle Swarm Optimization was used as a control optimization algorithm. A popular classical approach based on the manual optimization of the sole rated speed was used as baseline for the comparison of results. The optimization of all the three speed parameters and the use of intelligent optimization techniques represent the novelties of this paper. The study showed that the Bees Algorithm outperformed the other two optimization methods. The proposed algorithm was able to find speed parameters that greatly enhanced the power yield, without compromising the usage time or significantly increasing the capital costs. The comparison between the standard manual optimization method and the two intelligent optimization techniques proved the superiority of the latter ones.

A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production

To achieve burdening process optimization of copper strips effectively, a nonlinear constrained multi-objective model is established on the principle of the actual burdening. The problem is formulated with two objectives of minimizing the total cost of raw materials and maximizing the amount of waste material thrown into melting furnace. In this paper, a novel approach called “hybrid multi-objective artificial bee colony” (HMOABC) to solve this model is proposed. The HMOABC algorithm is new swarm intelligence based multi-objective optimization technique inspired by the intelligent foraging behavior of honey bees, summation of normalized objective values and diversified selection (SNOV-DS) and nondominated sorting approach. Two test examples were studied and the performance of HMOABC is evaluated in comparison with other nature inspired techniques which includes nondominated sorting genetic algorithm II (NSGAII) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate HMOABC approach is a powerful search and optimization technique for burdening optimization of copper strips.

A Cooperative Approach to Bee Swarm Optimization

A novel Cooperative Bee Swarm Optimization (CBSO) algorithm based on foraging behaviour of honey bees is presented. The CBSO employs cooperative behaviours of multiple swarms in optimizing numerical functions. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. The flying patterns provide an efficient way to balance exploration and exploitation. Cooperation is obtained by sharing information between swarms through a leader swarm. Also, a colonization process is performed between swarms. In colonization process a portion of an extinct swarm is replaced with the individuals from a colonist swarm. The proposed algorithm was tested on a set of well-known test functions. Results have shown that the proposed algorithm is efficient, robust, and outperforms other genetic, particle swarm, and bee algorithms examined in this paper.

Bee colony optimization for combined heat and power economic dispatch

This paper presents a novel optimization approach to the combined heat and power economic dispatch problem by using bee colony optimization algorithm. The algorithm is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The performance of the proposed algorithm is validated by illustration with a test system. The results of the proposed approach are compared with those of particle swarm optimization, real-coded genetic algorithm and evolutionary programing techniques. From numerical results, it is seen that bee colony optimization based approach is able to provide a better solution at a lesser computational effort.

A swarm intelligence approach to the quadratic minimum spanning tree problem

The quadratic minimum spanning tree problem (Q-MST) is an extension of the minimum spanning tree problem (MST). In Q-MST, in addition to edge costs, costs are also associated with ordered pairs of distinct edges and one has to find a spanning tree that minimizes the sumtotal of the costs of individual edges present in the spanning tree and the costs of the ordered pairs containing only edges present in the spanning tree. Though MST can be solved in polynomial time, Q-MST is NP -Hard. In this paper we present an artificial bee colony (ABC) algorithm to solve Q-MST. The ABC algorithm is a new swarm intelligence approach inspired by intelligent foraging behavior of honey bees. Computational results show the effectiveness of our approach.

Is Pollination at Risk? Current Threats to and Conservation of Bees

Insect pollination is an ecosystem service with high economic value that is mainly provided by bees. However, feral bees and managed honeybees are threatened globally by several anthropogenic factors such as changes in agricultural practices, loss of unimproved grasslands, urbanization, and spread of pathogens. These factors have led to the extinction of many native bees, to a reduction in the number of managed honeybee colonies, and put the nutritional bases for bees at risk. Chronic exposure to insecticides, even when they are appropriately used, may affect the foraging behavior of honeybees, which in turn weakens a colony and makes it more susceptible for diseases. Honeybees suffer from parasites like the Varroa mite, and from several viral and bacterial pathogens, which need to be controlled with great efforts. Climate changes represent future threats that will probably impact native bee populations as well as apicultural practices. Coordinated actions are needed to prevent the global loss of large pollinator populations. Especially the maintenance of plant and bee diversity is very important – as well as basic research on the causes for honeybee colony losses. Together, policy, science, and economy should have a crucial interest in conserving the global pollination service of bees.

Finite element model updating using bees algorithm

In this paper the application of bees algorithm (BA) in the finite element (FE) model updating of structures is investigated. BA is an optimization algorithm inspired by the natural foraging behavior of honeybees to find food sources. The weighted sum of the squared error between the measured and computed modal parameters is used as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. To this end, the modal parameters of the numerical model are compared with the experimental ones obtained through modal testing. Moreover, to verify the performance of BA, it is compared with the genetic algorithm, the particle swarm optimization and the inverse eigensensitivity method. Comparison of the results indicates that BA is a simple and robust approach that could be effectively applied to the FE model updating problems.

Investigating the performance of bees algorithm in solving quadratic assignment problems

One of the issues in the manufacturing industry is designing an arrangement of facilities where the layout can minimise the material-handling cost. This issue is known as a facility layout problem (FLP). To date, bees algorithm has evolved as a new meta-heuristic which was inspired by the food foraging behaviour of honey bees. This paper describes the first application of bees algorithm in solving FLP which has been modelled as quadratic assignment problem (QAP). A new bees algorithm is proposed and formulated, and it is tested using numerous problems taken from QAPLIB – an international renowned database that provides QAP cases. The performance of the proposed algorithm is evaluated in terms of its solution quality and computational time. Furthermore, the solutions generated by the algorithm are compared with the existing best known solutions published in the literature. The experimental results show that the algorithm is very successful in solving small problem instances which have up to 20 facilities.