Honey-bee Optimization Research Articles

Honey Bee Optimization based Zone Divided Wireless Sensor Network

Honey Bee Optimization based Zone Divided Wireless Sensor Network

A family of honey-bee optimization algorithms for Max-CSPs

This paper presents new approaches for maximal constraint satisfaction problems (Max-CSPs). Inspired by the honey- bee marriage process, our approaches try to reach a solution that satisfies as many constraints as possible in a reasonable period of time. Our approaches are a centralized and distributed honey-bee optimization enhanced by a new parameter called local optimum detector. The latter allows detection of the local optimum. In this paper, newer algorithms and their experimental results are presented.

Software Performance Testing Using Dynamic Load Generation in Cloud Environment

Cloud computing provides flexibility in allocating resources such as CPU and memory using virtual machines. A cloud application must provide better quality of service in terms of availability of memory, execution time and allocation of CPU. Virtualization helps to reduce the energy by allocating optimal number of physical machines. The workload between the virtual machines and the real machines must match in order to improve the performance of the cloud system. For this purpose, this paper proposes an Equal Priority and Cluster Based Load Balancing Algorithm (EPCLBA) for balancing the load in cloud computing environment that uses honey bee optimization technique for effective clustering of resources.In this work, a cluster of resources including servers and databases are allocated for user requests in order to perform load balancing. This helps to reduce the number of I/O operations leading to increase in performance and reduction in energy. Moreover, this paper changes the number of virtual machines depending on the type of applications. For CPU intensive applications, it allocates virtual machines with more CPU power. On the other hand for data intensive applications, it allocates machines with higher database features for enhanced storage. From the experiments carried out in this work, it is observed that the performance of the cloud system is depending on the type of workload allocation method used by the application. This system was tested using a ticket booking application and proved that virtual machine allocation using the proposed cluster based method reduces the I/O operations and enhances the processing power by reducing the time to process the requests.

Lifetime Enhancement of WSN using Energy-Balanced Distributed Clustering Algorithm with Honey Bee Optimization

Wireless sensor network consists of sensor nodes with sensing and communication capabilities. Typical communication model of wireless sensor network consists of several sensor nodes as group members, sensor head and sinks. Past few years have witnessed increased in the potential use of Wireless Sensor Network (WSN) such as military surveillance, tracking and monitoring, disaster management and combat field reconnaissance. Many issues in WSN are formulated as multidimensional optimization problem and solved through energy based distributed clustering techniques [1]. The main problem in WSN is the lifetime of network. To support scalability, nodes are often grouped in clusters having a leader, often referred as Sensor Head (SH). A SH is responsible for not only sending data to base station but also assists the general nodes to send sensed data to target nodes. The energy consumption of SH is greater than general nodes. Therefore SH selection will affect the lifetime of WSN and also support packet loss. This proposed approach is based on Energy-Balanced Distributed Clustering (EBDC) [2–3] algorithm. Honey bee optimization with some parameters is employed over EBDC algorithm for effective dynamic cluster head selection. This approach helps in increasing the energy consumption. This proposed technique works in three stages: Cluster nodes send data to SH, SH sends data directly to leader and leader sends data to Base Station (BS). Sensor Head (SH) has responsibility to select neighbor node as a Sub Sensor Head (SSH) to reduce overload. Simulations results demonstrate that EBDC-HBO algorithm improves the life time of network.

Materialized View Selection using Marriage in Honey Bees Optimization

Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization (MBO). An MBO based view selection algorithm (MBOVSA), which aims to select views that incur the minimum total cost of evaluating all the views (TVEC), is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.

A honey-bees optimization algorithm for a two-agent single-machine scheduling problem with ready times

In this paper we consider two agents that compete on the use of a common processor. Each of the agents has a set of jobs that have to be processed on the same machine without preemption. Each of the agents wants to minimize an objective function that depends on the completion time of its own jobs. In addition, each job has different release dates. In the presence of unequal release dates, it is sometimes advantageous to form a non-full batch, while in other situations it is a better strategy to wait for future job arrivals in order to increase the fullness of the batch. The objective is to find a schedule that performs well with respect to the objectives of both agents. To solve this difficulty problem, we construct a branch-and-bound solution scheme incorporating these bounds and some dominance rules for the optimal solution. In view of the advantage of combining local and global searches in the honey-bees optimization algorithm, we attempt to use a marriage in honey-bees optimization algorithm (MBO) to find near-optimal solutions. We conduct extensive computational experiments to evaluate the performance of the algorithms.

Algorithm of Marriage in Honey Bees Optimization Coperate with Linear Method

Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but it has the shortcomings of low speed and complex computation process. By changing the structure of MBO and utilizing a linear method to perform the local characteristic, we propose a new optimization algorithm. The global convergence characteristic of the proposed algorithm is proved by using the Markov Chain theory. And then some simulations are done on Traveling Salesman Problem (TSP) and several public evaluation functions. By comparing the proposed algorithm with MBO and Genetic Algorithm, simulation results show that the proposed algorithm has better convergence performance.

The single-machine total tardiness problem with unequal release times and a linear deterioration

Recently, machine scheduling problems with deteriorating jobs have received more attention from the scheduling research community. In this paper we consider a single-machine scheduling problem with a linear deteriorating effect and unequal release times. The objective is to minimize the total tardiness. We propose a branch-and-bound algorithm incorporating with several dominance properties and two lower bounds to search for the optimal solution. In addition, we propose a marriage in honey-bees optimization algorithm (MBO) to provide a near-optimal solution. The computational experiment is also conducted to evaluate the impacts of the parameters over the performances of the proposed algorithms.

Improved Honey Bee Optimization Based on Mutual Information in Sensor Network

Improved Honey Bee Optimization Based on Mutual Information in Sensor Network

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

Swarm Intelligence and Its Applications

Swarm Intelligence and Its Applications

Examining the role of perception, social and private information in honey bee foraging algorithms

The last decade has seen the development of a family of powerful optimisation algorithms inspired by the foraging behaviours of honey bees. A key component of these algorithms is the concept of 'recruitment' whereby successful foragers transmit information to other colony members concerning the location of discovered resources and thereby 'recruit' for that location. However, real-world foraging by honey bees is much more complex and embeds a number of additional features, including individual sensory 'perception', 'noisy' recruitment, and 'private information' memory. In this study, we develop a series of algorithms which embed these features and assess the impact of each on the effectiveness of the resulting search performance on a series of benchmark problems representing differing resource landscapes. The simulation results support findings from the empirical study of real-world honey bees that recruitment is not a unique driver of successful foraging activity and that private information also plays an important role. This finding is relevant for the design of honey bee optimisation algorithms.

An investigation on a two-agent single-machine scheduling problem with unequal release dates

This paper addresses a two-agent scheduling problem on a single machine with arbitrary release dates, where the objective is to minimize the tardiness of one agent, while keeping the lateness of the other agent below or at a fixed level Q. A mixed integer programming model is first presented for its optimal solution, admittedly not to be practical or useful in the most cases, but theoretically interesting since it models the problem. Thus, as an alternative, a branch-and-bound algorithm incorporating with several dominance properties and a lower bound is provided to derive the optimal solution and a marriage in honey-bees optimization algorithm (MBO) is developed to derive the near-optimal solutions for the problem. Computational results are also presented to evaluate the performance of the proposed algorithms.

Bee life-based multi constraints multicast routing optimization for vehicular ad hoc networks

A vehicular ad hoc network (VANET) is a subclass of mobile ad hoc networks, considered as one of the most important approach of intelligent transportation systems (ITS). It allows inter-vehicle communication in which their movement is restricted by a VANET mobility model and supported by some roadside base stations as fixed infrastructures. Multicasting provides different traffic information to a limited number of vehicle drivers by a parallel transmission. However, it represents a very important challenge in the application of vehicular ad hoc networks especially, in the case of the network scalability. In the applications of this sensitive field, it is very essential to transmit correct data anywhere and at any time. Consequently, the VANET routing protocols should be adapted appropriately and meet effectively the quality of service (QoS) requirements in an optimized multicast routing. In this paper, we propose a novel bee colony optimization algorithm called bees life algorithm (BLA) applied to solve the quality of service multicast routing problem (QoS-MRP) for vehicular ad hoc networks as NP-Complete problem with multiple constraints. It is considered as swarm-based algorithm which imitates closely the life of the colony. It follows the two important behaviors in the nature of bees which are the reproduction and the food foraging. BLA is applied to solve QoS-MRP with four objectives which are cost, delay, jitter, and bandwidth. It is also submitted to three constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth. In order to evaluate the performance and the effectiveness of this realized proposal using C++ and integrated at the routing protocol level, a simulation study has been performed using the network simulator (NS2) based on a mobility model of VANET. The comparisons of the experimental results show that the proposed algorithm outperformed in an efficient way genetic algorithm (GA), bees algorithm (BA) and marriage in honey bees optimization (MBO) algorithm as state-of-the-art conventional metaheuristics applied to QoS-MRP problem with the same simulation parameters.

蜜蜂の蜜源選択における閾値調整機能を模した最適化アルゴリズム

蜜蜂の蜜源選択における閾値調整機能を模した最適化アルゴリズム

SMBO: A self-organizing model of marriage in honey-bee optimization

This paper proposes a novel swarm intelligence technique, which is an adaptation of Abbass’s marriage in honey-bee optimization (MBO), with the aim to achieve better overall performance than the original version of the MBO while also lowering the computation time for finding the optimal solution. The original MBO has been proven to be one of the best swarm intelligence algorithms for solving optimization problems. However, many parameters need to be properly set in order for the MBO to perform at its best. Therefore, long computation time caused by a large number of trial and error iterations involved in trying to find the right combination of parameters is unavoidable. The framework of the proposed algorithm is similar to the original MBO, which is based on the marriage behavior of honey-bees. In order to improve the efficiency of the MBO algorithm, several aspects of the original MBO have been adapted, such as (1) the proposed algorithm is adapted to obtain the ability to automatically search for the proper number of queens, (2) the proposed algorithm divides the problem space into several colonies, each of which has its own queen. In order to keep the number of colonies to a minimum, the proposed algorithm, therefore, encourages the queens to compete with each other for a larger colony and also urges the newly-born brood which is fitter than the queen of the colony to overthrow the queen. (3) the fuzzy c-means algorithm is employed to assign the drones to the proper colonies. The proposed algorithm has been evaluated and compared to the original MBO algorithm. The experimental results on six benchmark problems demonstrate the potential of the proposed algorithm in offering an efficient and effective solution to the problem.

A Modified Marriage in Honey-Bee Optimization for Function Optimization Problems

Many researches have implemented a genetic algorithm with real-coded chromosomes to solve a wide variety of problems. Their experimental results suggested that the real-coded genetic algorithms gave superior results to binary-coded genetic algorithm on most of the test problems. Inspired by the above founding, this study aims to (1) propose a modified Marriage in Honey-bee Optimization (MBO) technique (2) compare the performance of the proposed technique to that of the real-coded GA technique. In this study, two main ideas are proposed. Firstly, to handle the real encoding of genotypes, we present a new crossover operator and a new heuristic worker, named the scroll-based worker, for manipulating the real value of genes. Secondly, to reduce the number of user-defined parameters, we provide the original MBO with a self-organizing capability. With a self-organizing capability, the proposed model can automatically determine the proper number of queens itself. The experimental results on five benchmark test functions show that the proposed model is very effective in solving the function optimization problems.

Discrete bee algorithms and their application in multivariable function optimization

In this paper we present four discrete versions of two different existing honey bee optimization algorithms: the discrete artificial bee colony algorithm (DABC) and three versions of the discrete fast marriage in honey bee optimization algorithm (DFMBO1, DFMBO2, and DFMBO3). In these discretized algorithms we have utilized three logical operators, i.e. OR, AND and XOR operators. Then we have compared performances of our algorithms and those of three other bee algorithms, i.e. the artificial bee colony (ABC), the queen bee (QB), and the fast marriage in honey bee optimization (FMBO) on four benchmark functions for various numbers of variables up to 100. The obtained results show that our discrete algorithms are faster than other algorithms. In general, when precision of answer and number of variables are low, the difference between our new algorithms and the other three algorithms is small in terms of speed, but by increasing precision of answer and number of variables, the needed number of function evaluations for other algorithms increases beyond manageable amounts, hence their success rates decrease. Among our proposed discrete algorithms, the DFMBO3 is always fast, and achieves a success rate of 100% on all benchmarks with an average number of function evaluations not more than 1010.

Special issue on Nature Inspired Cooperative Strategies for Optimisation (NICSO)

This special issue collects a selection of the best papers emerging from the 2007 International Workshop on Nature inspired cooperative strategies for optimisation (NICSO 2007). Nature inspired strategies are being used in ever more widely ranging settings spanning not only computational problem solving (e.g. Neural Networks, Simulated Annealing, Ant Colony Optimisation, etc.) but also on a variety of other disciplines such as chemistry, physics and engineering. Moreover, cooperative strategies are gaining momentum across many computer science disciplines such as machine learning, classification, data mining, and, of course, optimisation. The NICSO workshop series and its associated publications have been actively helping to advance the state of the art in Nature inspired cooperative strategies for optimisation since its inception in 2006. Some, but not all, of the main themes covered by NICSO include, computational studies in adaptive behavior, amorphous computing, artificial life, ant colonies optimisation, artificial immune systems, swarm intelligence, software selfassembly and self-organisation, evolutionary algorithms, neural networks, etc. as applied to numerical, combinatorial, non linear, dynamic and/or noisy optimisation. NICSO 2007 received 83 papers, out of which 46 were published in the proceedings under Springer’s Studies in Computational Intelligence series. From these 46 papers 12 were invited to the special issue in Natural Computing for which authors were asked to submit extended and improved versions of their works. Submitted papers went through a rigorous two (in some cases three) rounds of additional independent reviewing. Each paper was assigned three or more reviewers. From the 12 papers received, the following five were accepted for this thematic issue. In ‘‘Honey Bees Mating Optimisation Algorithm for Large Scale Vehicle Routing Problems’’ by Y. Marinakis et al., the authors present a sophisticated multi-hybrid algorithm for the vehicle routing problem. Their algorithm combines two metaheuristics and one heuristic to produce a balanced allocation of effort between local and global search. The Honey Bee Optimisation algorithm that is seeded with an improved variation of

Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms

Integrated circuit (IC) is a vital component of most electronic commodity. IC manufacturing in Taiwan is booming, with revenues from the ICs industry having grown significantly in the recent years. Given the nature of technology, capital intensity and high value-added, accurate forecasting of IC the industry output can improve the competitivity of IC cooperation. Support vector regression (SVR) is an emerging forecasting scheme that has been successfully adopted in many time-series forecasting areas. Additionally, the data preprocessing procedure and the determination of SVR parameters significantly impact the forecasting accuracy of SVR models. Thus, this work develops a support vector regression model with scaling preprocessing and marriage in honey-bee optimization (SVRSMBO) model to accurately forecast IC industry output. The scaling preprocessing procedure is utilized to lower the fluctuation of input data, and the marriage in honey-bees optimization (MBO) algorithm is adopted to determine the three parameters of the SVR model. Numerical data collected from the previous literature are used to demonstrate the performance of the proposed SVRSMBO model. Simulation results indicate that the SVRSMBO model outperforms other forecasting models. Hence, the SVRSMBO model is a promising means of forecasting IC industry output.