Financial health of many organizations now-a-days is being affected by investment in software and their cost estimation. Therefore, to provide effective cost estimation models are the most complex activity in software engineering fields. This paper presents a fuzzy clustering and optimization model for software cost estimation. The proposed model uses Pearson product-moment correlation coefficient and one-way ANOVA analysis for selecting several effort adjustment factors. Further, it applies fuzzy C-means clustering algorithm for project clustering. Then, parameters of COCOMO model have been optimized using Multi-objective Genetic Algorithm (MOGA). Here, two objectives are considered. One is to minimize the Mean Magnitude of Relative Error (MMRE) and other is to maximize the Prediction (PRED). This model has been tested on the COCOMO dataset. The optimization result has also been compared with Multi-objective Particle Swarm Optimization (MOPSO) algorithm. The result has proved superiority of MOGA in parameter optimization for getting strength back the accuracy of software cost estimation.
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