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Applications of Bee Colony Optimization - Durham e …

V. Tereshko, A. Loengarov, "Collective decision-making in honey bee foraging dynamics", Comput. Inf. Syst. J., 2005, pp.1-7.

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Artificial Bee Colony (ABC) Algorithm Homepage

By finding the coverage area and sensing range, the network lifetime maximized and reduces the energy consumption

Key words: Wireless Sensor Networks (WSN'S), Ant Colony Optimization, Connective Coverage, Network Lifetime.

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 (2013) Applications of Bee Colony Optimization. Masters thesis, Durham University.

"ACO (Ant Colony Optimization)" will provide you with many famous scholars' homepage, this scholars are all take ACO as part of their research interests. This part comes from Appendix B of the book “Ant Colony Algorithms: Theory and Applications”, which was written by Dr. Haibin DUAN, and published by Science Press in Dec. 2005.

Artificial Bee Colony Algorithm for Solving Optimal …

International Journal of Biological, Ecological and Environmental Sciences (IJBEES), 2012.

To demonstrate the validity of the proposed algorithm, computer simulations are carried out on actual power network of Kerman Province, Iran and the simulation results are presented and discussed.

Key words: Distribution systems, Loss Sensitivity Factors, Capacitor placement, Artificial Bee Colony Algorithm

[1] C.

A brief historical overview and a comparative study is presented for some approaches used in solving MLP problem with the solution obtained in the weighting approach with two cases collateral numerical illustrative examples.

Key words: Multi level decision making; hierarchical structures; weighting approach; scalar optimization problem; analytic hierarchy process

[1] G.

A comparative study of Artificial Bee Colony algorithm

thesis submitted to the college of Agriculture, University of Sulaimani.[5] Beersma J.J.

In this paper we presents the different techniques of Ant Colony Optimization (ACO) that are improves the routing mechanism.

Key words: MANET, Ant, Pheromones, Survey, ACO

[1] C.-K.

Most projects of these natures have not been adopted by the small farmers, either because the final design and data collection procedures are frequently inappropriate or the cost has remained inaccessible and the subsequent transfer of technology from researcher to the end user has been anything but effective.

In recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely discussed.
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  • Bee colony based parameter selection for subpixel mapping

    Optimization, color conversion, gradient and mesh algorithms are image processing techniques have been implemented.

  • ** Ant-Colony & Bee-Colony Optimization ** ANSYS, MSC Marc, ..

    Implementation of an efficient Zig-Bee based Train Anti-Collision for railways is being proposed in this paper.

  • (ABC) Algorithm on FPGA for Real-Time Applications, MSc


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Ant colony optimization algorithms - Wikipedia

V. Tereshko, "Reaction-diffusion model of a honeybee colony’s foraging behavior", in: M. Schoenauer, e t al. (Eds.), Parallel Problem Solving from Nature VI, Lecture Notes in Computer Science, vol. 1917, Springer- Verlag, Berlin, 2000, pp. 807–816.

Master Thesis (In Turkish), 2010.

V. Tereshko, T. Lee ,"How information mapping patterns determine foraging behavior o f a honey bee colony", Open Syst. Inf. Dyn. 9, 2002, pp.181–1 93.

The Scientific World Journal is a ..

Many computationally difficult problems are attacked using non-exact algorithms, such as approximation algorithms and heuristics. This thesis investigates an ex- ample of the latter, Bee Colony Optimization, on both an established optimization problem in the form of the Quadratic Assignment Problem and the FireFighting problem, which has not been studied before as an optimization problem. Bee Colony Optimization is a swarm intelligence algorithm, a paradigm that has increased in popularity in recent years, and many of these algorithms are based on natural pro- cesses.
We tested the Bee Colony Optimization algorithm on the QAPLIB library of Quadratic Assignment Problem instances, which have either optimal or best known solutions readily available, and enabled us to compare the quality of solutions found by the algorithm. In addition, we implemented a couple of other well known algorithms for the Quadratic Assignment Problem and consequently we could analyse the runtime of our algorithm.
We introduce the Bee Colony Optimization algorithm for the FireFighting problem. We also implement some greedy algorithms and an Ant Colony Optimization al- gorithm for the FireFighting problem, and compare the results obtained on some randomly generated instances.
We conclude that Bee Colony Optimization finds good solutions for the Quadratic Assignment Problem, however further investigation on speedup methods is needed to improve its performance to that of other algorithms. In addition, Bee Colony Optimization is effective on small instances of the FireFighting problem, however as instance size increases the results worsen in comparison to the greedy algorithms, and more work is needed to improve the decisions made on these instances.

Mathematical and Natural Sciences

B. Basturk, D. Karaboga,"An artificial bee colony (ABC) algorithm for numeric function optimization", Applied Soft Computing, 8(1), 2008, pp. 687-697

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