Search

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

A number of software programms are developed in several releases. Before developing any new release, a set of requirements is suggested for inclusion in the release. Having multiple constraints, it is impossible to develop all the requirements proposed in the next release. The presence of competing companies, replication of product ideas, shortening of the development time and lack of project funding will reduce the cost of developing a release. Developer teams should select a subset of the proposed requirements for development that would provide their clients with the highest amount of satisfaction despite the deadline limitations or cost constraints. The existence of conflicting goals and other constraints makes this choice very complicated. In this paper, an algorithm is introduced which is based on a fuzzy inference system to determine the suitability of each requirement for development in the next release. The proposed algorithm, rather than the developer team, takes the responsibility to select the optimal subset of requirements for the development of the next release. Experimental results of the proposed algorithm are then compared with the results of the genetic algorithm. The subset selected by the proposed algorithm provides much more satisfaction than the genetic algorithm. [ABSTRACT FROM AUTHOR]