Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

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A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

A comparative study of Artificial Bee Colony algorithm

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A modified Artificial Bee Colony algorithm for real-parameter optimization

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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A survey: algorithms simulating bee swarm intelligence

Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.

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A Brief Review of Nature-Inspired Algorithms for Optimization

Solving a NP-Complete problem precisely is spiny: the combinative explosion is the ransom of this accurateness. It is the reason for which we have often resort to approached methods assuring the obtaining of a good solution in a reasonable time. In this paper we aim to introduce a new intelligent approach or meta-heuristic named “Bees Swarm Optimization”, BSO for short, which is inspired from the behaviour of real bees. An adaptation to the features of the MAX-W-SAT problem is done to contribute to its resolution. We provide an overview of the results of empirical tests performed on the hard Johnson benchmark. A comparative study with well known procedures for MAX-W-SAT is done and shows that BSO outperforms the other evolutionary algorithms especially AC-SAT, an ant colony algorithm for SAT.

Cooperative bees swarm for solving the maximum weighted satisfiability problem

DNA cryptography is a new promising direction in cryptography research that emerged with the progress in DNA computing field. DNA can be used not only to store and transmit information, but also to perform computations. The massive parallelism and extraordinary information density inherent in this molecule are exploited for cryptographic purposes, and several DNA based algorithms are proposed for encryption, authentification and so on. The current main difficulties of DNA cryptography are the absence of theoretical basis, the high tech lab requirements and computation limitations. In this paper, a symmetric key bloc cipher algorithm is proposed. It includes a step that simulates ideas from the processes of transcription (transfer from DNA to mRNA) and translation (from mRNA into amino acids). This algorithm is, we believe, efficient in computation and very secure, since it was designed following recommendations of experts in cryptography and focuses on the application of the fundamental principles of Shannon: Confusion and diffusion. Tests were conducted and the results are very satisfactory.

An encryption algorithm inspired from DNA

Feature selection is often used before a data mining or a machine learning task in order to build more accurate models. It is considered as a hard optimization problem and metaheuristics give very satisfactory results for such problems. In this work, we propose a hybrid metaheuristic that integrates a reinforcement learning algorithm with Bee Swarm Optimization metaheuristic (BSO) for solving feature selection problem. QBSO-FS follows the wrapper approach. It uses a hybrid version of BSO with Q-learning for generating feature subsets and a classifier to evaluate them. The goal of using Q-learning is to benefit from the advantage of reinforcement learning to make the search process more adaptive and more efficient. The performances of QBSO-FS are evaluated on 20 well-known datasets and the results are compared with those of original BSO and other recently published methods. The results show that QBO-FS outperforms BSO-FS for large instances and gives very satisfactory results compared to recently published algorithms.

QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection.

Feature selection is an important data-preprocessing step that often precedes the classification task. Because of large amount of features in real world applications, feature selection is considered as a hard optimization problem. For such problems, metaheuristics have been shown to be a very promising solving approach. In this work, we propose to use Bee Swarm Optimization (BSO) for feature selection. The proposed algorithm, BSO-FS, is based on the wrapper approach that uses BSO for the generation of feature subsets, and a classifier algorithm to evaluate the solutions. BSO-FS is tested on well-known datasets and its performances are compared with those of recently published methods. Obtained results show that for the majority of datasets, BSO-FS selects efficiently relevant features while improving the classification accuracy.

BSO-FS: Bee Swarm Optimization for Feature Selection in Classification

In this paper, we present a parallel version of the Bees Swarm Optimisation (BSO) metaheuristic. Firstly, we present a sequential algorithm, then we expose the original and innovative approach used for its parallelisation. Experiments comparing the performances of the sequential and the parallel algorithms in solving instances of the weighted maximum satisfiability problem are presented. A comparison is also done between BSO and other metaheuristics performances.

Souhila Sadeg

Papers

Cooperative bees swarm for solving the maximum weighted satisfiability problem

An encryption algorithm inspired from DNA

QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection.

BSO-FS: Bee Swarm Optimization for Feature Selection in Classification

A selective approach to parallelise Bees Swarm Optimisation metaheuristic: application to MAX-W-SAT