Estimation Effort using Developed Cat Swarm Optimization

Abstract

Estimation is one of the most important factors in building and designing software. This research to provide a study of the principles of reducing the cost of software and understanding how to apply these techniques to the general programs. The swarm intelligence algorithms have been used because they have community-based methods where there is a range of potential solutions in this community. The ideal solution is sought through repetitive steps, these methods are based on solving complex problems by simple factors without central control or a comprehensive model and possess distributed control, the most important characteristics of the swarm intelligence, It will produce a self-contained, adaptive, scalable, flexible, powerful, parallel, self-regulatory and cost-effective system. The traditional cat algorithm has been developed by assuming that the flavour is a location within the coordinates (X, Y) to reduce the search time and to find the optimal solution. The results obtained from the implementation of the tool display the adoption of (NASA) data Excellence than the developed cats swarm optimization (DCSO) algorithm, In terms of shortening the number of search cycles and the time required to reach the best solution, The best rate was the error(0.25), by fixing the search ratio and the highest fitness value for the number of successful cat copies and increasing the number of repetitions of implementation and the stability of the search site and speed. The tool built Swarm Effort Estimation Tool --SEET Using a language (Matlab 2017) The tool is described based on (Enterprise Architect 9).