Abstract: Planning is the most vital part of project management. It describes the resources that we need to complete the project successfully. Software cost estimation is a part of planning. It describes the estimated cost and time required to complete the project. The input in software cost estimation is the size of the code and cost drivers. The output is the Effort in terms person per month. Our proposed model is for tuning parameters of COCOMO model software cost estimation using Multi Objective (MO) Particle Swarm Optimization (PSO). We will be using clustering methods to divide the data items into number of clusters and PSO be used then for parameter tuning of each cluster. The clusters and the tuned parameters will be trained on Neural Network by back propagation algorithm. The results will be compared for the improvement of the previous work.
Keywords: Mutiobjective (MO) Particle Swarm Optimization (PSO), integrated development environment (IDE), Constructive cost model (COCOMO), SWARM intelligence.
Title: Enhancement of Multi-Objective Particles Swarm Optimization Using Clustering Techniques
Author: Amita Talyan, Anoop kumar, Rajan sachdeva
International Journal of Engineering Research and Reviews
ISSN 2348-697X (Online)
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