Ant colony optimization-based bio-inspired hardware: survey and prospect

Duan, Haibin; Yuan, Ya-xiang; Zou, Jie; Feng, Xiaoyi

doi:10.1177/0142331210366689

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…In most SI-based computation algorithms, the computational resources are allocated to a set of relatively simple agents. These agents exploit a form of indirect communication mediated by the environment to find an optimal solution (Duan et al, 2010b). Therefore, the typical challenges for the SI-based hardware can be generalised as: online realisation, robustness, generalisation, disaster problems, and implementation.…”

Section: Si Inspired Hardware and Networkmentioning

confidence: 99%

New progresses in swarm intelligence-based computation

Duan

Luo

2015

IJBIC

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Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.

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Section: Si Inspired Hardware and Networkmentioning

confidence: 99%

New progresses in swarm intelligence-based computation

Duan

Luo

2015

IJBIC

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“…Moreover, ACO is applied in some other areas for problem solving and optimisation. Classification rules (Martens et al, 2006), Bayesian networks (DeCampos et al, 2002(DeCampos et al, , 2008, protein folding (Shmygelska and Hoos, 2005), radial distribution networks (Abdelaziz et al, 2012), hardware optimisation (Duan et al, 2012) and data mining (Parpinelli et al, 2002) are some examples of these areas. More information about applications of ACO can be found in study of ChandraMohan and Baskaran (2012) and Dorigo and Stutzle (2004).…”

Section: Applications Of Acomentioning

confidence: 99%

Ant colony optimisation for vehicle traffic systems: applications and challenges

Jabbarpour

Malakooti

Noor

et al. 2014

IJBIC

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Ant-based algorithms simulate the cooperative behaviour of real ants in finding food resources. A significant number of studies have focused on the self-organised behaviour of ants in the natural environment to develop effective systems for dynamic problems. Ant-based systems have special properties such as scalability, adaptability, and dynamicity, which are the main requirements for solving vehicle traffic congestion problem. Thus, ant-based algorithms are now being adopted by vehicle traffic systems (VTSs) to guide vehicles to less congested paths. However, literature shows that comprehensive reviews are lacking in this field. The main contribution of this paper is the review and classification of the most relevant systems based on novel taxonomy. A survey that includes statistical analyses on ant-based VTS was conducted to identify the limitations and evaluation process of VTS. This paper concludes by proposing a general framework in applying ant colony optimisation to VTS. . He received his Master of Data Communication and Networking in 2012 at the same university. His current research interests include ad hoc networks, quality of service, swarm intelligence and vehicle traffic routing systems.Hossein Malakooti received his Master of Data Communication and Networking at the

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“…ANT COLONY OPTIMIZATION TECHNIQUES Ant Colony Optimization (ACO) is a probabilistic search approach based on an evolutionary process which is biologically inspired by the foraging behavior of some ant species colonies [3], [4] and [5].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony optimization algorithm for breast cancer cells classification

Machraoui¹,

Cherni²,

Sayadi³

2013

2013 International Conference on Electrical Engineering and Software Applications

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Abstract-Ant colony optimization (ACO) is a bio-inspired technique formalized into a meta-heuristic for combinatorial optimization problems. In this work, the ACO-Otsu segmentation method, based on ACO algorithm and Otsu's method as a fitness function, is applied in classification and detection of breast cancer cells. Subsequently, this method is compared with the Otsu's standard method. The experiments show the performance of this probabilistic search approach in such type of problems.

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Ant colony optimization-based bio-inspired hardware: survey and prospect

Cited by 15 publications

References 18 publications

New progresses in swarm intelligence-based computation

New progresses in swarm intelligence-based computation

Ant colony optimisation for vehicle traffic systems: applications and challenges

Ant Colony optimization algorithm for breast cancer cells classification

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