Algorithm of Fast Marriage in Honey Bees Optimization and Convergence Analysis

Yang, Chenguang; Chen, Jie; Tu, Xuyan

doi:10.1109/ical.2007.4338865

Cited by 30 publications

(16 citation statements)

References 4 publications

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“…Fathian et al (2007); Fathian and Amiri (2008) used this two-stage paradigm in order to overcome local optima problem in clustering and compared the algorithm with other heuristic algorithms in clustering. Yang et al (2007a) modified MBO by enhancing global convergence capability of the MBO because the calculation process is complex and the speed is slow and named Fast Marriage in Honey Bees Optimization (FMBO). By randomly initializing drones and restricting the condition of iteration, they made the computation process easier and faster.…”

Section: Collective Decision and Nest Site Selectionmentioning

confidence: 99%

A survey: algorithms simulating bee swarm intelligence

2009

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Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

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Section: Collective Decision and Nest Site Selectionmentioning

confidence: 99%

A survey: algorithms simulating bee swarm intelligence

2009

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“…This algorithm was inspired by the mating process of honey bees (Yang et al 2007). The algorithm is presented in the following.…”

Section: The Fast Marriage In Honey Bee Optimizationmentioning

confidence: 99%

Discrete bee algorithms and their application in multivariable function optimization

Salim

Vakil-Baghmisheh

2010

Artif Intell Rev

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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.

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“…In the literature survey some studies modeled the honeybee foraging or finding home to be used in optimization problem [2]. Other works have proposed models based on the marriage behavior of honeybee [4].From these models there being extracted many features were being utilized by engineering and computer science [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection System (IDS) by Using Honeybee Concepts and Framework

Ali¹

2015

The 7th International Conference on Information Technology

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Abstract-Intrusion Detection System has been studied for more than ten years. Though Artificial Intelligence (AI) techniques have been integrated to improve IDS but the success is still far from satisfaction. Thus, we believe a new strategy to improve IDS is badly needed. One of the solutions is by imitating the honeybee colony that can successfully protect their colony. In fact, the honeybee colony system and problems are quite similar with network system, and the way the bees protecting their colony can also be considered similar with the IDS in the network system. In this paper, we investigated the honeybee colony system as well as their detection system to get improvement methods for IDS engine in order to enhance IDS system for a better network defense. The adaptation of the honeybee protection and defense system itself is a new knowledge that can help other systems such as antivirus, antimalware, or even defense system to imitate the AI techniques in performing their functions. We train the proposed system with different types of attacks data and model different types of attack signatures. The performance of the proposed IDS is evaluated using NSL-KDD data set. The experiments show that the performance of the proposed model can detect novel intrusions and reduce false alarms.

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Algorithm of Fast Marriage in Honey Bees Optimization and Convergence Analysis

Cited by 30 publications

References 4 publications

A survey: algorithms simulating bee swarm intelligence

A survey: algorithms simulating bee swarm intelligence

Discrete bee algorithms and their application in multivariable function optimization

Enhancing Intrusion Detection System (IDS) by Using Honeybee Concepts and Framework

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