Vehicular ad-hoc network (VANET) is a key enabling technology of intelligent transportation systems. VANETs are characterised by the rapidly changing topology and the unbounded network size. These characteristics pres...
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Vehicular ad-hoc network (VANET) is a key enabling technology of intelligent transportation systems. VANETs are characterised by the rapidly changing topology and the unbounded network size. These characteristics present a range of challenges to different VANET applications such as routing and security. Clustering has strongly presented itself as an efficient solution to such challenges. In this study, the authors formulate the clustering algorithm as a many-objective optimisation problem. Then, they propose a unified framework to optimise the configuration parameters arbitrary clustering algorithms. Three many-objective metaheuristic optimisation techniques, ESPEA, MOEA/DD and NSGA-III, are compared in context of this framework, and various commonly used quality indicators are utilised to identify the metaheuristic with the best quality of solutions. The proposed framework is then used to optimise a recent clustering algorithm. Using the optimal configuration resulting from the proposed framework significantly improves the performance of the clustering algorithm under-test compared to the non-optimised algorithm as well as other clustering approaches. This is demonstrated by the simulation results which showed up to 182% improvement in the cluster head lifetime and a reduction of 36% in the clustering packets overhead in the highway environment.
Test suite minimisation is a process that seeks to identify and then eliminate the obsolete or redundant test cases from the test suite. It is a trade-off between cost and other value criteria and is appropriate to be...
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Test suite minimisation is a process that seeks to identify and then eliminate the obsolete or redundant test cases from the test suite. It is a trade-off between cost and other value criteria and is appropriate to be described as a many-objective optimisation problem. This study introduces a mutation score (MS)-guided many-objectiveoptimisation approach, which prioritises the fault detection ability of test cases and takes MS, cost and three standard code coverage criteria as objectives for the test suite minimisation process. They use six classical evolutionary many-objectiveoptimisation algorithms to identify efficient test suite, and select three small programs from the Software-Artefact Infrastructure Repository (SIR) and two larger program space and gzip for experimental evaluation as well as statistical analysis. The experiment results of the three small programs show non-dominated sorting genetic algorithm II (NSGA-II) with tuning was the most effective approach. However, MOEA/D-PBI and MOEA/D-WS outperform NSGA-II in the cases of two large programs. On the other hand, the test cost of the optimal test suite obtained by their proposed MS-guided many-objectiveoptimisation approach is much lower than the one without it in most situation for both small programs and large programs.
Although many-objectiveoptimisation can be simplified through reduction of redundant objectives, algorithms that perform this reduction still lack a convenient method of evaluation. In this paper, we address this def...
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Although many-objectiveoptimisation can be simplified through reduction of redundant objectives, algorithms that perform this reduction still lack a convenient method of evaluation. In this paper, we address this deficiency by proposing a new method of evaluation, on the basis of changes in the Pareto-domination ratio after a reduction has occurred. Experimental results have shown that the proposed method can perform non-redundant objective set evaluation more accurately than existing evaluation methods, and also does not need the true Pareto front beforehand.
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