Enhanced micro genetic algorithm-based models for multi-objective optimization
| dc.contributor.author | Tan, Choo Jun | |
| dc.date.accessioned | 2015-05-26T08:29:22Z | |
| dc.date.available | 2015-05-26T08:29:22Z | |
| dc.date.issued | 2014-09 | |
| dc.description.abstract | Multi-objective Optimization Problems (MOPs) entail multiple conflicting objectives to be satisfied simultaneously. As such, a set of alternative solutions that is able to satisfy all objectives with respect to the Pareto optimality principle is desired. Besides that, the quality of good MOP solutions needs to strike a balance between convergence and diversity against the true Pareto front (i.e. distribution of the ideal Pareto optimal solutions). This research is concerned with how evolutionary algorithms can be employed to undertake MOPs with good convergence and diversity properties of the solutions with respect to the true Pareto front. Specifically, three evolutionary models based on the micro Genetic Algorithm (mGA) have been developed in a sequential manner for undertaking MOPs. Firstly, a Modified mGA (MmGA) model is introduced. MmGA aims to search for the Pareto optimal solutions efficiently and improve the convergence score of the solutions towards the Pareto front in tackling MOPs. Secondly, an ensemble of MmGA models is proposed to improve the robustness of individual MmGA models and, at the same time, to accelerate the convergence score of the solutions towards the Pareto front. Thirdly, to take both convergence and diversity scores of the Pareto optimal solutions into consideration, a multi-agent system that utilizes the Trust-Negotiation-Communication (TNC) reasoning scheme is exploited. Multiple performance indicators are incorporated into the TNC-based MmGA model, in order to achieve good convergence and diversity scores. The computational time complexity of three proposed evolutionary models is examined using the O-notation analysis. It is found that, while the computational time complexity of the three proposed models is higher than that of mGA, all proposed models have the same computational xxiii time complexity with that of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is embedded into all proposed models to improve their performances. Based on a number of MOP benchmark problems, all proposed models are able to yield favorable results as compared with those from their predecessors in term of convergence and diversity scores of the solutions. A number of experiments with multi-objective classification problems also indicate improvement of the classifier performances and, at the same time, reduction of the number of features used in classification, as compared with the results from standard classifiers as well as from other methods published in the literature. The potential of the proposed MmGA-based models in undertaking practical problems has also been demonstrated using four real-world MOPs. All performance indicators of the proposed MmGA-based models are quantified using the bootstrap statistical method. The outcomes positively ascertain the usefulness of the proposed evolutionary models in undertaking MOPs. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/756 | |
| dc.subject | Algorithm-based Models | en_US |
| dc.title | Enhanced micro genetic algorithm-based models for multi-objective optimization | en_US |
| dc.type | Thesis | en_US |
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