7,701 research outputs found
Applications of Bee Colony Optimization
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
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring
The Artificial Bee Colony (ABC) is the name of an optimization algorithm that
was inspired by the intelligent behavior of a honey bee swarm. It is widely
recognized as a quick, reliable, and efficient methods for solving optimization
problems. This paper proposes a hybrid ABC (HABC) algorithm for graph
3-coloring, which is a well-known discrete optimization problem. The results of
HABC are compared with results of the well-known graph coloring algorithms of
today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of
the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive
experimentations has shown that the HABC matched the competitive results of the
best graph coloring algorithms, and did better than the traditional heuristics
EA-SAW when solving equi-partite, flat, and random generated medium-sized
graphs
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization
Memetic computation (MC) has emerged recently as a new paradigm of efficient
algorithms for solving the hardest optimization problems. On the other hand,
artificial bees colony (ABC) algorithms demonstrate good performances when
solving continuous and combinatorial optimization problems. This study tries to
use these technologies under the same roof. As a result, a memetic ABC (MABC)
algorithm has been developed that is hybridized with two local search
heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction
exploitation (RWDE). The former is attended more towards exploration, while the
latter more towards exploitation of the search space. The stochastic adaptation
rule was employed in order to control the balancing between exploration and
exploitation. This MABC algorithm was applied to a Special suite on Large Scale
Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary
Computation. The obtained results the MABC are comparable with the results of
DECC-G, DECC-G*, and MLCC.Comment: CONFERENCE: IEEE Congress on Evolutionary Computation, Brisbane,
Australia, 201
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