Honey Bee Swarm Research Articles

Artificial bee colony algorithm to design two-channel quadrature mirror filter banks

Artificial bee colony (ABC) algorithm has been introduced recently for solving optimization problems. The ABC algorithm is based on intelligent foraging behavior of honeybee swarms and has many advantages over earlier swarm intelligence algorithms. In this work, a new method based on ABC algorithm for designing two-channel quadrature mirror filter (QMF) banks with linear phase is presented. To satisfy the perfect reconstruction condition, low-pass prototype filter coefficients are optimized to minimize an objective function. The objective function is formulated as a weighted sum of four terms, pass-band error, and stop-band residual energy of low-pass analysis filter, square error of the overall transfer function at the quadrature frequency and amplitude distortion of the QMF bank. The design results of the proposed method are compared with earlier reported results of particle swarm optimization (PSO), differential-evolution (DE) and conventional optimization algorithms.

Self-generated fuzzy systems design using artificial bee colony optimization

In this paper, artificial bee colony (ABC) optimization based methodology is proposed for automatically extracting Takagi–Sugeno (TS) fuzzy systems with enhanced performance from data. The design procedure aims to find the structures and the parameters of the TS fuzzy systems simultaneously without knowing the rule number as a priori. In the proposed method, a fuzzy system is encoded into a food source with appropriate string representation so that the TS model is entirely specified. The encoded premise and consequent parameters of the fuzzy model evolve together through artificial bee colony optimization strategy simulating the global foraging behavior of honey bee swarm so that good solutions can be achieved. Simulations on benchmark modeling and tracking control problems are performed and compared with other existing methods. The experimental results indicate that the proposed ABC optimization based fuzzy systems design algorithms can successfully find accurate fuzzy models with appropriate number of rules. Moreover, the proposed approach outperforms the compared methods and can provide considerable improvements in tackling complex modeling and tracking control problems.

Africanized honeybees in urban areas: a public health concern.

This study aimed to investigate the occurrence of Africanized honeybees in Botucatu, São Paulo, Brazil, and to implement a program to remove such swarms. The occurrences of Africanized honeybee swarms between 2010 and 2012 were studied and strategies to prevent accidents were developed. We noted 1,164 cases of Africanized honeybee occurrences in the city, and 422 swarms were collected. The developed strategies to prevent accidents were disseminated to the population. We contributed to reducing the risks represented by Africanized honeybee swarms in urban areas, by collecting swarms and disseminating strategic information for preventing accidents.

Decision-making in honeybee swarms based on quality and distance information of candidate nest sites

In the nest-site selection process of honeybee swarms, an individual bee performs a waggle dance to communicate information about direction, quality, and distance of a discovered site to other bees at the swarm. Initially, different groups of bees dance to represent different potential sites, but eventually the swarm usually reaches an agreement for only one site. Here, we model the nest-site selection process in honeybee swarms of Apis mellifera and show how the swarms make adaptive decisions based on a trade-off between the quality and distance to candidate nest sites. We use bifurcation analysis and stochastic simulations to reveal that the swarm’s site distance preference is moderate>near>far when the swarms choose between low quality sites. However, the distance preference becomes near>moderate>far when the swarms choose between high quality sites. Our simulations also indicate that swarms with large population size prefer nearer sites and, in addition, are more adaptive at making decisions based on available information compared to swarms with smaller population size.

Flying insect swarms

Dublon and Sumpter give a concise and access guide to flying insect swarms, some of which, such as the crop-eating locust swarms, are pests, while others, such as honeybee swarms and migrating butterflies, are beneficial.

Enriching Electric Power Quality by using Bee Algorithm in Shunt Active Filters

The application of soft computing technique is growing fast in the area of power electronics and drives. The Bee optimization technique is considered as a new technique for power-quality (PQ) devices. The main objective of this paper is to present the Bee optimization method and Multiple Synchronous Reference Frame (MSRF) Theory in cascaded multilevel inverter for harmonic elimination. The foremost aim in Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) strategy is eliminating low-order harmonics, while the fundamental component is satisfied. In this paper, the Bee algorithm (BA) and MSRF theory is applied to a 9-level cascaded multilevel inverter using MATLAB software. The BA is based on the food foraging manners of a swarm of honeybees and it performs a neighborhood search joint with a random search. This method has higher accuracy and possibility of convergence. Simulation results prove supremacy of BA over MSRF theory in attaining perfect overall minima and higher convergence rate. Finally their results are compared and verified for a distorted three phase power system utility.

Reduction of artificial bee colony algorithm for global optimization

Abstract This paper presents a reduction of artificial bee colony algorithm for global optimization. Artificial bee colony algorithm is an optimization technique which refers to the behavior of honeybee swarms, and a multi-point search approach which finds a best solution using multiple bees. For avoiding local minima, a number of bees are initially prepared and their positions are updated by artificial bee colony algorithm. Bees sequentially reduce to reach a predetermined number of them grounded in the evaluation value and artificial bee colony algorithm continues until the termination condition is met. In order to show the effectiveness of the proposed algorithm, we examine the best value by using test functions compared to existing algorithms. Furthermore the influence of best value on the initial number of bees for our algorithm is discussed.

Particle bee algorithm for tower crane layout with material quantity supply and demand optimization

Abstract The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, ready-mixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA, BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms.

Improve Performance of Load Balancing using Artificial Bee Colony in Grid Computing

Grid computing is a novel approach which solves the load balancing problems in scientific, engineering and research area. Load Balancing is a technique that can be used to improve resource utilization, to reduce MAKESPAN and to minimize number of failures. In grid environment, different algorithm for resources and data distribution is used to increase the performance and efficiency of load balancing. In grid environment Static threshold and PSO are used for load balancing. Static (fixed) threshold i.e. 3 is used for data transfer from source node to server node. Then, using PSO for data transferring that is better than, static threshold. Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm. In this paper, propose a new load balancing algorithm using Artificial Bee Colony(ABC) for obtaining minimum makespan and less number of failure then, obtained results are presented and compared with static threshold and PSO.

Improved Artificial Bee Colony Algorithm and Its Application in LQR Controller Optimization

In order to get the optimal performance of controller and improve the design efficiency, artificial bee colony (ABC) algorithm as a metaheuristic approach which is inspired by the collective foraging behavior of honey bee swarms is considered for optimal linear quadratic regulator (LQR) design in this paper. Furthermore, for accelerating the convergence speed and enhancing the diversities of population of the traditional ABC algorithm, improved solution searching approach is proposed creatively. The proposed approach refers to the procedure of differential mutation in differential evolutionary (DE) algorithm and produces uniform distributed food sources in employed bee phase to avoid local optimal solution. Meanwhile, during the onlooker bees searching stage where the solution search area has been narrowed by employed bees, new solutions are generated around the solution with higher fitness value to keep the fitness values increasing monotonously. The improved ABC algorithm is applied to the optimization of LQR controller for the circular-rail double inverted pendulum system, and the simulation results show the effect on the proposed optimization problem.

Distribution System Network Reconfiguration by using Artificial Bee Colony Algorithm.

This paper presents a new methodology using artificial bee's colony algorithm (ABC) in the radial distribution systems for the placement and size of Distributed Generators (DG). The ABC algorithm is a new population based meta heuristic approach exalted by intelligent foraging behavior of honey bee swarm. This paper presents a feeder reconfiguration problem with Distributed Generation to the distribution system. In the recent years the application of Distributed Generation (DG) has found increasing attention. Optimal reconfiguration needs selection of the best set of branches to be opened so that the resulting radial distribution system has improved voltage profile system and minimized power loss. The developed methodology is demonstrated by a radial distribution system with distributed generation. The study results show that the optimal on/off patterns of the switches can be identified which give the minimum power loss while respecting all the constraints.

Protein-Ligand Docking Using Artificial Bee Colony Algorithm

Protein-ligand docking is one of the most significant issue in structure-based drug design (SBDD). Generally, this docking is considered as an optimization problem which specifies the energetically stable conformation of the ligand at the binding site. However, it is very difficult to identify the correct pose because of many optimization parameters with high correlation. In previous studies, it has been reported that popular docking programs (Glide [1] , GOLD [2] , FlexX [3] , AutoDock [4] , etc.) can identify the correct docking pose with an accuracy of only about 60% [5] . In this work, we attempted to apply Artificial Bee Colony algorithm [6] (ABC) to docking. ABC is an optimization algorithm based on the intelligent behavior of honey bee swarm, which has a higher global ability than other algorithms such as Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO). The performance of the ABC for docking is evaluated for several protein-ligand complexes including some highly flexible ligands in comparison with other algorithms. The results reveal that the ABC might be more suitable for docking than others in particular for dealing with highly flexible ligands.

A multi-agent system for scalable group decision making

In Group Decision Support System (GDSS), multicast group communication was found to be challenging because of the nature of its distributed decision makers. The efficiency and correctness of the group decision making process mainly depends on the communication approach used among its participants. Although many solutions have been proposed to handle group communication, this paper aims to achieve reliable, fault tolerant, and high scalable group communication that supports successful group decision making. In this paper, a new group communication approach that supports group decision making in collaborative environments such as GDSS is proposed. This approach enables a set of distributed decision makers to collaborate and exchange proposals and solutions in a coordinated and reliable way to reach a collective decision. The proposed approach is a multi-agent system inspired by the behaviour of swarms of honeybees in their food foraging and source evaluation. The advantages of the proposed approach are investigated and showed that simulating bee behavior for group decision making makes the new system fault tolerant and more reliable compared to centralized and distributed approaches. The ability of the new approach to handle a group decision making with several environmental changes is measured and compared through simulation experiments. The results showed that the new multi-agent system achieved higher scalability with 45% efficiency. 5544 Anas Jebreen Atyeeh Husain

A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main idea of HABC is to enhance the information exchange between bees by introducing the crossover operator of genetic algorithm to ABC. With suitable crossover operation, valuable information is fully utilized and it is expected that the algorithm can converge faster and more accurate. Eight versions of HABC algorithm combined by different selection and crossover methods under the model were proposed and tested on several benchmark functions. Then, the settings of the new parameter crossover rate for two well performed HABC versions are tested to verify their best values. Finally, four rotated functions and four shifted functions are used to test the performance of the two algorithms on complex functions and asymmetric functions. Experiment results showed that these two versions of HABC algorithm offer significant improvement over the original ABC and are superior to other two state of the art algorithms on some functions.

Two modified versions of artificial bee colony algorithm

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm.

Artificial bee colonies for continuous optimization: Experimental analysis and improvements

The artificial bee colony (ABC) algorithm is a recent class of swarm intelligence algorithms that is loosely inspired by the foraging behavior of honeybee swarms. It was introduced in 2005 using continuous optimization problems as an example application. Similar to what has happened with other swarm intelligence techniques, after the initial proposal, several researchers have studied variants of the original algorithm. Unfortunately, often these variants have been tested under different experimental conditions and different fine-tuning efforts for the algorithm parameters. In this article, we review various variants of the original ABC algorithm and experimentally study nine ABC algorithms under two settings: either using the original parameter settings as proposed by the authors, or using an automatic algorithm configuration tool using a same tuning effort for each algorithm. We also study the effect of adding local search to the ABC algorithms. Our experimental results show that local search can improve considerably the performance of several ABC variants and that it reduces strongly the performance differences between the studied ABC variants. We also show that the best ABC variants are competitive with recent state-of-the-art algorithms on the benchmark set we used, which establishes ABC algorithms as serious competitors in continuous optimization.

Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera

Reproductive swarms of honeybees are faced with the problem of finding a good site to establish a new colony. We examined the potential effects of swarm size on the quality of nest-site choice through a combination of modelling and field experiments. We used an individual-based model to examine the effects of swarm size on decision accuracy under the assumption that the number of bees actively involved in the decision-making process (scouts) is an increasing function of swarm size. We found that the ability of a swarm to choose the best of two nest sites decreases as swarm size increases when there is some time-lag between discovering the sites, consistent with Janson & Beekman (Janson & Beekman 2007 Proceedings of European Conference on Complex Systems, pp. 204-211.). However, when simulated swarms were faced with a realistic problem of choosing between many nest sites discoverable at all times, larger swarms were more accurate in their decisions than smaller swarms owing to their ability to discover nest sites more rapidly. Our experimental fieldwork showed that large swarms invest a larger number of scouts into the decision-making process than smaller swarms. Preliminary analysis of waggle dances from experimental swarms also suggested that large swarms could indeed discover and advertise nest sites at a faster rate than small swarms.

A mechanism for value-sensitive decision-making.

We present a dynamical systems analysis of a decision-making mechanism inspired by collective choice in house-hunting honeybee swarms, revealing the crucial role of cross-inhibitory ‘stop-signalling’ in improving the decision-making capabilities. We show that strength of cross-inhibition is a decision-parameter influencing how decisions depend both on the difference in value and on the mean value of the alternatives; this is in contrast to many previous mechanistic models of decision-making, which are typically sensitive to decision accuracy rather than the value of the option chosen. The strength of cross-inhibition determines when deadlock over similarly valued alternatives is maintained or broken, as a function of the mean value; thus, changes in cross-inhibition strength allow adaptive time-dependent decision-making strategies. Cross-inhibition also tunes the minimum difference between alternatives required for reliable discrimination, in a manner similar to Weber's law of just-noticeable difference. Finally, cross-inhibition tunes the speed-accuracy trade-off realised when differences in the values of the alternatives are sufficiently large to matter. We propose that the model, and the significant role of the values of the alternatives, may describe other decision-making systems, including intracellular regulatory circuits, and simple neural circuits, and may provide guidance in the design of decision-making algorithms for artificial systems, particularly those functioning without centralised control.

A New Method for Ranking Hotels Based on Group Decision Making in Honeybee Swarms

Currently, studies on group decision making are attracting considerable attention in many fields. With the increasing use of the Internet, there is an abundance of information available easily to us. However, it is difficult to evaluate accurately the reliability of such information, because much of it is colored by preferences and bias or opinion. It is therefore of interest to develop a group decisionmaking algorithm that can make appropriate decisions by processing this kind of information. Group decision-making algorithms are employed in many situations, a typical example being the evaluation of a hotel’s overall quality. However, current methods of evaluating hotels are not always reliable, because a guest may not be able to evaluate all hotels, and different guests have different preferences, or “inclinations.” As such, it is difficult to select the best hotel from a review of hotel rankings. In this paper, in an attempt to solve this problem, we focus on decision making that takes place in a honeybee swarm to select a nesting site. Although each bee has different inclinations, the honeybee swarm is capable of choosing the best nesting site. We applied this advantage to generate hotel rankings. We then conducted simulations that assumed practical application and confirmed that reviewers agreed on the top hotels, and the reliability of the top rankings improved, at the expense of decreased reliability of the lower rankings.

Parameter identification for Van Der Pol–Duffing oscillator by a novel artificial bee colony algorithm with differential evolution operators

This paper is concerned with the uncertain parameters’ identification of Van Der Pol–Duffing oscillators (VDPD) through a non-Lyapunov way with ABC and ABCDEO respectively by converting the problem into a multiple modal non-negative functions’ minimization, which is of vital significance and attracts increasing interests in various research fields. Firstly, a novel artificial bee colony (ABC) algorithm with differential evolution operators (ABCDEO) is introduced to accelerate ABC due to the slow nature of the collective intelligence of honey bee swarms, by doing differential evolution operators to the employed bees colony in a certain probability, a special region contraction rules, and swarm re-initialization strategy based on number-theory nets. Secondly, the unknown parameters of VDPD for three main physical states, the cases with noise, and a comprehensive set of twelve complex benchmark functions including a wide range of dimensions, are employed for experimental verification. Experimental results confirm that the ABCDEO is a successful method in VDPD’s parameters’ identification.