Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higherdimensional optimisation problems.

Firefly algorithm: recent advances and applications

International Joint Conference on Neural Networks

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu’s method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods.

Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

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.

Vector quantization using the firefly algorithm for image compression

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.

Ming-Huwi Horng

Papers

Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

Vector quantization using the firefly algorithm for image compression

Multilevel minimum cross entropy threshold selection based on the firefly algorithm

A multilevel image thresholding using the honey bee mating optimization

Texture feature coding method for classification of liver sonography