Enhanced bees algorithms

Dissertant

Hamad, Amaal Ghazi

University

University of Technology

Faculty

-

Department

Computer Sciences Department

University Country

Iraq

Degree

Master

Degree Date

2008

English Abstract

Many complex multi-variable optimization problems cannot be solved exactly within polynomial bounded computation times.

This generates much interest in search algorithms that find near-optimal solutions in reasonable running times. Swarm-based algorithms such as Bees Algorithm BA which is an optimization algorithm that mimics the food foraging behavior of swarms of honey bees have proven to be very powerful computational techniques due to their search capabilities.

Other methods found useful in diverse application areas are simulated annealing SA; evolution strategies etc.

The searching ability of these algorithms can be improved by properly blending their characteristic features.

In this work attempts to modify BA are made, first by intermixing the search properties of BA and SA into a single global platform called Bees' Simulated Annealing BSA, secondly by modifying it's original structure into a more controlled framework called Exploration-Balanced Bees Algorithm EBBA, thirdly by combining the two previously mentioned modifications BSA and EBBA into a general powerful search technique called Exploration-Balanced Bees' Simulated Annealing EBBSA, in order to develop hybrid algorithms which are equally applicable and have a better searching ability and power to reach a near optimal solution.

This leads to the development of fast methods to solve complicated types of optimization problems. Four NP-hard optimization problems have been selected and used in this work to compare the performances of the original BA and it's modifications. Experimental results implemented in visual basic show that all the modifications has the ability to solve NP-hard optimization problems within acceptable amount of time with a faster convergence and time reduction obtained than the original BA.

Best results were obtained by using EBBA which hows that it's the one with the most promising performance, Experiments with the different kind of parameters used within the proposed algorithms to Identify the population size-optimal solution convergence relationship surest that it is typically sufficient to apply a small constant number of bees to achieve high performance.

In addition to the right parameters choice made, it shows that the usage of smaller population achieves raster convergence and more time reduction.

Main Subjects

Information Technology and Computer Science

Topics

• Algorithms

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-305370