Ant colony optimization approach for classification of occupational low back disorder risks

Akay, Diyar; Toksarı, Murat

doi:10.1002/hfm.20134

Cited by 17 publications

(15 citation statements)

References 27 publications

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“…First, a fraction of the pheromone evaporates, and then each ant deposits an amount of pheromone that is proportional to the fitness of its solution. This process is iterated until a stopping criterion is met [4,[109][110][111].…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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First performed in 1954, organ transplantation is a universally practiced clinical procedure. This study uses ant colony optimization (ACO), radial basis function neural network (RBFNN), Kohonen's self-organizing maps (SOM), and support vector machines (SVMs) to examine the effect of various cognitive, psychographic, and attitudinal factors on organ donation. ACO, RBFNN, SOM, and SVMs are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are altruistic values, perceived risks/benefits, knowledge, attitudes toward organ donation, and intention to donate organs. The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering complex patterns in the dataset and also shows the classification and clustering abilities of machine-learning systems.

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Section: Ant Colony Optimizationmentioning

confidence: 99%

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Mostafa

Toksarı

2011

Neural Comput & Applic

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“…Tabach et al [13] used the artificial neural network simulation metamodeling to assess groundwater contamination in a road project. Akay and Toksari [14] presented a metamodel approach based on ACO for classification problem when Toksari [15] studied on a metamodel approach based on ACO for energy demand estimation. Toksari et al [16] proposed the metamodel approach using variable neighborhood search to forecast transport energy demand.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

Kesen

Toksarı

Güngör

et al. 2009

Computers & Operations Research

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“…Evaporation of pheromone after each tour and many reiterations of the process means eventually only the shortest tour remains, or one close to it (Dorigo et al, 1996). The ant-based system has since been widely adopted for use in other combinatorial optimisation problems (Liang and Smith, 2004;Akay and Toksari, 2009;Zhang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Optimisation in a natural system: Argentine ants solve the Towers of Hanoi

Reid

Sumpter

Beekman

2011

Journal of Experimental Biology

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SUMMARYNatural systems are a source of inspiration for computer algorithms designed to solve optimisation problems. Yet most 'natureinspired' algorithms take only superficial inspiration from biology, and little is known about how real biological systems solve difficult problems. Moreover, ant algorithms, neural networks and similar methods are usually applied to static problems, whereas most biological systems have evolved to perform under dynamically changing conditions. We used the Towers of Hanoi puzzle to test whether Argentine ants can solve a potentially difficult optimisation problem. We also tested whether the ants can adapt to dynamic changes in the problem. We mapped all possible solutions to the Towers of Hanoi on a single graph and converted this into a maze for the ants to solve. We show that the ants are capable of solving the Towers of Hanoi, and are able to adapt when sections of the maze are blocked off and new sections installed. The presence of exploration pheromone increased the efficiency of the resulting network and increased the ants' ability to adapt to changing conditions. Contrary to previous studies, our study shows that mass-recruiting ant species such as the Argentine ant can forage effectively in a dynamic environment. Our results also suggest that novel optimisation algorithms can benefit from stronger biological mimicry.

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Ant colony optimization approach for classification of occupational low back disorder risks

Cited by 17 publications

References 27 publications

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Market segmentation of organ donors in Egypt: a bio-inspired computational intelligence approach

Analyzing the behaviors of virtual cells (VCs) and traditional manufacturing systems: Ant colony optimization (ACO)-based metamodels

Optimisation in a natural system: Argentine ants solve the Towers of Hanoi

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