Bee colony optimization thesis

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.

Bee colony optimization python

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. Login An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction Habib Shah, Habib Shah An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction. Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. Masters thesis, Durham University. 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. Item Type:. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony ABC algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron MLP. Different equations are used to guide the network for providing an accurate result with less training and testing error. Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification.

Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony ABC algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron MLP.

Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. 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.

Bee colony optimization ppt

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. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony ABC algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron MLP. Item Type:. 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. Masters thesis, Durham University. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training algorithms focus on weight values, activation functions, and network structures for providing optimal outputs. However, some difficulties arise where the BP cannot get achievements without trapping in local minima and converge very slow in the solution space. Item Type:. 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.

Item Type:. Item Type:.

Ant colony optimization

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. Item Type:. Login An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction Habib Shah, Habib Shah An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction. 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. Moreover, these improved algorithm's success to get high accuracy and optimize the best network's weight values for training the MLP. 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. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony ABC algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron MLP. 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. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification.

Login An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction Habib Shah, Habib Shah An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction.

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. Preview Kb Abstract Many computationally difficult problems are attacked using non-exact algorithms, such as approximation algorithms and heuristics.

From the experimental analysis, the proposed improved algorithms get better the classification efficacy for time series prediction and Boolean function classification.

artificial bee colony algorithm matlab code ppt
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Applications of Bee Colony Optimization