Honey Bee Mating Research Articles

Economic Dispatch Using Parameter-Setting-Free Harmony Search

Economic dispatch is one of the popular energy system optimization problems. Recently, it has been solved by various phenomenon-mimicking metaheuristic algorithms such as genetic algorithm, tabu search, evolutionary programming, particle swarm optimization, harmony search, honey bee mating optimization, and firefly algorithm. However, those phenomenon-mimicking problems require a tedious and troublesome process of algorithm parameter value setting. Without a proper parameter setting, good results cannot be guaranteed. Thus, this study adopts a newly developed parameter-setting-free technique combined with the harmony search algorithm and applies it to the economic dispatch problem for the first time, obtaining good results. Hopefully more researchers in energy system fields will adopt this user-friendly technique in their own problems in the future.

Image Enhancement through intensity of edge pixels using Honey Bee Mating Optimization

There are many techniques of image enhancement as well as optimizing the same process in the image processing such as Powell’s method, Fast Ostu’s method, Histogram equalization (HE) method, Particle swarm optimization (PSO) and Honey bee (HB) algorithm. In this paper, a new approach to automatic image enhancement using honey bee mating optimization is implemented by specifying intensity of the edges pixels and also earlier reported PSO results were used. Further results of previous work i-e PSO and HBMO are compared through different parameters. The obtained results indicate that the proposed Honey bee algorithm yields better results in the terms of both the maximization of number of the pixels in the edges and peak signal to noise ratio (PSNR). Computational time is also relatively small in the Honey bee enhanced image as compared to the PSO case which is earlier reported. Keyword: Swarm optimization, honey bee mating optimization and image enhancement.

Modelling honey bee queen mating as a measure of feral colony density

Robust estimates of feral and wild honey bee (Apis mellifera) colony densities are essential for the understanding of the role of honey bees in an ecosystem, and for planning responses to an incursion of an exotic honey bee disease or pest. New genetic methods make it possible to identify the number of feral colonies with which a test queen mates. We present an agent-based model of the spatial process of honey bee mating that describes the relationship between feral colony densities and the number of unique colonies with which a queen mates. This model incorporates random, aggregated and overdispersed spatial distributions of feral colonies and drone congregation areas. We model different densities of feral colonies and drone congregation areas with various numbers of test queens and determine the range of counts of matings with drones from unique colonies. The model shows that the range of counts for a given set of parameters is consistent with a Poisson distribution, and that the most significant explanatory variable is density of the feral colonies. The results obtained reveal that 10 or more test queens may be needed in field studies to resolve order of magnitude differences in feral colony densities. We conclude that the new genetic methods provide a powerful tool for making indirect inferences about feral colony densities, so long as the field survey results are treated as outcomes of a stochastic spatial process.

Bi-objective task assignment in heterogeneous distributed systems using honeybee mating optimization

Effective task assignment is critical for achieving high performance in heterogeneous distributed computing systems. However, there is a possibility of processor and network failures and this can have an adverse impact on applications running on such systems. This paper proposes a new technique based on the honeybee mating optimization (HBMO) algorithm for static task assignment in the systems, which takes into account both minimizing the total execution and communication times and maximizing the system reliability simultaneously. The HBMO based approach combines the powers of simulated annealing, genetic algorithms, and an effective local search heuristic to search for the best possible solution to the problem under investigation within a reasonable computing time. We study the performance of the algorithm over a wide range of parameters such as the number of tasks, the number of processors, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness and efficiency of our algorithm are manifested by comparing it with recently proposed algorithms from the literature.

Rebuttal to “Discussion of ‘A new honey bee mating optimization algorithm for non-smooth economic dispatch’ Taher Niknam et al. Energy 36 (2011) 896–908”

Rebuttal to “Discussion of ‘A new honey bee mating optimization algorithm for non-smooth economic dispatch’ Taher Niknam et al. Energy 36 (2011) 896–908”

Impact of wind generators on distribution feeder reconfiguration

This paper presents a new algorithm for multiobjective distribution feeder reconfiguration (DFR) problem, while uncertainties of the load and wind generators are considered in the stochastic framework. In order to take the uncertainties into account, a probabilistic distribution load flow technique is adopted using point estimate method. The proposed multiobjective framework can optimize different objective functions including total power losses and voltage deviations simultaneously. As the result of conflicting behavior of these objective functions, a max-min decision maker is used to model operators’ preferences for trading off between these objectives. In addition, a novel and powerful optimization algorithm based on improved honey bee mating optimization is proposed to handle the multiobjective DFR problem sufficiently. Finally, the proposed framework is applied on two test systems in order to prove its efficiency and feasibility in comparison with the other methods in the area.

Discussion of “A new honey bee mating optimization algorithm for non-smooth economic dispatch” Taher Niknam et al. Energy 36 (2011) 896–908

This paper presents a discussion of “A new honey bee mating optimization algorithm for non-smooth economic dispatch” Taher Niknam et al. Energy 34 (2009) 757–766. The discussed paper presented economic dispatch problem by experimenting three standard test systems considering 10, 15, and 40-units. However, in the reported results of test system 1, the total fuel cost calculations are different for the given generation schedule. In this discussion, clarification regarding cost calculation is presented.

Advertisement timeout driven bee's mating approach to maintain fair energy level in sensor networks

In wireless sensor network, dynamic cluster-based routing approach is widely used. Such practiced approach, quickly depletes the energy of cluster heads and induces the execution of frequent re-election algorithm. This repeated cluster head re-election algorithm increases the number of advertisement messages, which in turn depletes the energy of overall sensor network. Here, we proposed the Advertisement Timeout Driven Bee's Mating Approach (ATDBMA) that reduces the cluster set-up communication overhead and elects the standby node in advance for current cluster head, which has the capability to withstand for many rounds.Our proposed ATDBMA method uses the honeybee mating behaviour in electing the standby node for current cluster head. This approach really outperforms the other methods in achieving reduced number of re-election and maintaining fair energy nodes between the rounds.

HBMO algorithm for calibrating water distribution network of Langarud city

In this paper a new meta-heuristic approach based on the nature of honey bees mating has been used for the calibration of a real city in the north of Iran named Langarud. This city has a population of nearly 68,000 people and about 43,000 water consumers. Langarud's area is about 900 km(2). The method was used to determine the Hazen-Williams roughness factor of the main pipes of the town and a correction factor for the nodal demands in the main nodes of the network.

Impact of Fuel Cell Power Plants on Multi‐objective Optimal Operation Management of Distribution Network

AbstractThis paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO‐DE) to solve the multi‐objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi‐objective problem by assuming that the decision‐maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method.

A combined approach for clustering based on K-means and gravitational search algorithms

Clustering is an attractive and important task in data mining that is used in many applications. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The gravitational search algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this paper, we present a hybrid data clustering algorithm based on GSA and k-means (GSA-KM), which uses the advantages of both algorithms. The GSA-KM algorithm helps the k-means algorithm to escape from local optima and also increases the convergence speed of the GSA algorithm. We compared the performance of GSA-KM with other well-known algorithms, including k-means, genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), honey bee mating optimization (HBMO), particle swarm optimization (PSO) and gravitational search algorithm (GSA). Five real and standard datasets from the UCI repository have been used to demonstrate the results of the algorithms. The experimental results are encouraging in terms of the quality of the solutions and the convergence speed of the proposed algorithm.

Optimization Generation Expansion Planning by HBMO

The generation expansion planning (GEP) problem is a largescale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of honey bee mating optimization for solving the generation expansion planning problem is presented. In the formulation, the objective is to minimize investment cost. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem

A hybrid clustering method is proposed in this paper based on artificial immune system and simulated annealing. An integration of simulated annealing and immunity-based algorithm, combining the merits of both these approaches, is used for developing an efficient clustering method. Tuning the parameters of method is investigated using Taguchi method in order to select the optimum levels of parameters. Proposed method is implemented and tested on three real datasets. In addition, its performance is compared with other well-known meta-heuristics methods, such as ant colony optimization, genetic algorithm, simulated annealing, Tabu search, honey-bee mating optimization, and artificial immune system. Computational simulations show very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required, comparing with mentioned methods.

Design–operation optimisation of run-of-river power plants

This paper addresses a strategy for the optimal design, control and operation of small hydropower (run-of-river (RoR) power) plants with the honey bee mating optimisation (HBMO) algorithm, while taking into account optimal design of the associated penstock as well as the turbines' number, type and their operation in the system. Civil engineering and electromechanical cost-effectiveness and constraints in an expected stream flow are also considered. The optimisation is driven by an objective function that includes the annual difference between generated energy, operating costs and depreciation costs for both initial investment and operation costs, considering various performance and hydraulic constraints. The HBMO algorithm specifies the annual benefit of generated energy and simultaneously determines the annualised operating cost. The solution includes selection of turbine types, number of turbines, penstock diameter, as well as scheduling the operation of an RoR power plant that results in maximum annualised benefit for a given set of river inflow histograms. The results of the proposed algorithm, which are compared with those of an analytical approach using Lagrange multipliers (LM), highlight the advantages in design, effective operation, ease of application and capability of the proposed HBMO algorithm for solving complex problems of this type.

Navigational Path Planning of MultiRobot using Honey Bee Mating Optimization Algorithm 'HBMO'

Over the last decade, evolutionary and meta-heuristic algorithms have been extensively developed and used as search and optimization tools in various problem domains, including science, commerce, and engineering. Their broad applicability, ease of use, and global perspective may be considered as the primary reason for their success. The honey-bees mating process may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bees mating. In this paper we present an alternative approach for navigational path plan of multi robot using HBMO algorithm. We reveal that this proposed optimization scheme outperforms other Evolutionary algorithms like Particle swarm optimization, Differential Evolutionary algorithm in the task of navigation.

A honey-bee mating optimization algorithm for educational timetabling problems

In this work, we propose a variant of the honey-bee mating optimization algorithm for solving educational timetabling problems. The honey-bee algorithm is a nature inspired algorithm which simulates the process of real honey-bees mating. The performance of the proposed algorithm is tested over two benchmark problems; exam (Carter’s un-capacitated datasets) and course (Socha datasets) timetabling problems. We chose these two datasets as they have been widely studied in the literature and we would also like to evaluate our algorithm across two different, yet related, domains. Results demonstrate that the performance of the honey-bee mating optimization algorithm is comparable with the results of other approaches in the scientific literature. Indeed, the proposed approach obtains best results compared with other approaches on some instances, indicating that the honey-bee mating optimization algorithm is a promising approach in solving educational timetabling problems.

Vector quantization using the firefly algorithm for image compression

The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde–Buzo–Gray (LBG) algorithm always generated local optimal codebook. Recently, particle swarm optimization (PSO) was adapted to obtain the near-global optimal codebook of vector quantization. An alterative method, called the quantum particle swarm optimization (QPSO) had been developed to improve the results of original PSO algorithm. The honey bee mating optimization (HBMO) was also used to develop the algorithm for vector quantization. In this paper, we proposed a new method based on the firefly algorithm to construct the codebook of vector quantization. The proposed method uses LBG method as the initial of FF algorithm to develop the VQ algorithm. This method is called FF-LBG algorithm. The FF-LBG algorithm is compared with the other four methods that are LBG, particle swarm optimization, quantum particle swarm optimization and honey bee mating optimization algorithms. Experimental results show that the proposed FF-LBG algorithm is faster than the other four methods. Furthermore, the reconstructed images get higher quality than those generated form the LBG, PSO and QPSO, but it is no significant superiority to the HBMO algorithm.

Alternative design redundancy allocation using an efficient heuristic and a honey bee mating algorithm

This study presents a new method to reduce the redundancy in serial-parallel system with linear constraints. The complexity of this problem is known as NP-hard and therefore the optimal solution is not accessible in the normal course of events. The modeling formulation deals with the allocation of different units/components with various reliabilities and characteristics and the objective is to maximize the total reliability. We develop an efficient heuristic which could efficiently compete with other existing methods in the literature. Also a honey bee mating optimization algorithm is developed for the first time to solve the redundancy allocation problems with large number of constraints. The proposed methods are tested on various test problems especially the 33 existing benchmark instances in the literature. The preliminary results indicate that the proposed method yield promising results in a reasonable amount of time.

A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources

Electrical generators of renewable electricity resources are quiet, clean and reliable. Optimal placement of renewable electricity generators (REGs) results in reduction of objective functions like losses, costs of electrical generation and voltage deviation. Because of recent technology developments of photovoltaic units, wind turbine and fuel cell units, only these generators are considered in this paper. This work presents a multiobjective optimization algorithm for the siting and sizing of renewable electricity generators. The objectives consist of minimization of costs, emission and losses of distributed system and optimization of voltage profile. This multiobjective optimization is solved by the Improved honey bee mating optimization (HBMO) algorithm. In the proposed algorithm, an external repository is considered to save non-dominated (Pareto) solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. This algorithm is executed on a typical 70-bus test system. Results of the case study show the proper siting and sizing of REGs are important to improve the voltage profile, reduce costs, emission and losses of distribution system. The main feature of the algorithm refers to its accuracy and calculation speed.

Multilevel minimum cross entropy threshold selection based on the firefly algorithm

The minimum cross entropy thresholding (MCET) has been widely applied in image thresholding. The search mechanism of firefly algorithm inspired by the social behavior of the swarms of firefly and the phenomenon of bioluminescent communication, is used to search for multilevel thresholds for image segmentation in this paper. This new multilevel thresholding algorithm is called the firefly-based minimum cross entropy thresholding (FF-based MCET) algorithm. Four different methods that are the exhaustive search, the particle swarm optimization (PSO), the quantum particle swarm optimization (QPSO) and honey bee mating optimization (HBMO) methods are implemented for comparison with the results of the proposed method. The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5. The need of computation time of using the FF-based MCET algorithm is the least, meanwhile, the results using the FF-based MCET algorithm is superior to the ones of PSO-based and QPSO-based MCET algorithms but is not significantly different to the HBMO-based MCET algorithm.