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Title:	Multi-population artificial bee colony algorithm for dynamic optimisation problems algorithm
Authors:	Shams K. Nseef (P74144)
Supervisor:	Salwani Abdullah, Prof. Dr.
Keywords:	Dynamic optimisation problems Algorithm Bee colony Dissertations, Academic -- Malaysia
Issue Date:	18-Jun-2015
Description:	Many real-world optimisation problems have the characteristics of being changeable over time in term of for instance, decision variables, constraints and objective function. These problems are known in the literature as dynamic optimisation problems (DOPs). A DOP requires an optimisation algorithm that can dynamically adapt itself to the new changes and tracking the optimum solution during the execution of the algorithm. The occurrences of such problems have attracted researchers into studying areas of artificial intelligence and operational research. Population-based algorithms, which are a set of methodologies that utilise a population of solutions distributed over the search space, have attracted considerable attention to handle DOPs, due to their good performance. Despite their good performance, in practice, the challenges in developing any optimisation algorithm for DOPs, which requires further research efforts, is how to maintain population diversity during the optimisation process in order to keep track of landscape changes. The work presented in this thesis proposes two variants of population-based algorithm called artificial bee colony algorithm (ABC), which is inspired by the foraging behaviour of real bee colonies, to solve DOP. The proposed ABC variants are based on the implementation of a multi-population strategy to maintain and provide diversity to the search process as well as maintaining multiple populations in different areas in the search space. In the first variant, an adaptive multi-population ABC in which the number of multi-population is dynamically adjusted based on the changing strength in the problem environment to efficiently cope with the changes over the run is proposed. The second variant utilises two populations in an interleaved manner where the external population is responsible for exploring the search landscape while the internal focuses on the search around the best solution. To test the performance of the proposed ABC, an experimental work is conducted on the DOP known as the moving peaks benchmark with a different number of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, when compared with a basic ABC and to the best known results in the scientific literature.,Master of Computer Science
Pages:	102
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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