Integration of Diversity Enhancement of Particle Swarm Optimization and Neighbourhood Search with k radius to predict Software Cost Estimation
Keywords:
Particle Swarm Optimization (PSO), Diversity Enhanced Particle Swarm Optimization (DPSO), Neighborhood Search (NS), Root Mean Square Error (RMSE).Abstract
Prediction of software development cost is a crucial activity in software engineering community at early stages of software development of life cycle. It helps to project manager to do better project management i.e. effective planning,organizing and monitoring. Generally, inaccurate estimation cost due to lack of data and inherent relationship between attributes. For accurate software cost estimation, the amount of techniques has been proposed, one of them is Particle Swarm Optimization (PSO) has been exposed an impressive performance. However, it is struck at local minima due to diversity loss quickly. In order to improve its searching ability and convergence rate, this paper proposes a new hybrid approach iscalled DPSONS-K. It consists a diversity enhanced method and neighborhood search methods with k radius. Where k is tuning parameter used to achieve stability between searching and convergence abilities.Seven benchmark datasets are used to investigate outcome of proposed approach.Comparative study shows that DPSONS-k approach achieved better results than other ones.
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http://promise.site.uottawa.ca/SERepository/datasets/cocomo81.arff
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