Browsing by Author "Astorga, Gino"

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Citation - WoS: 5
Citation - Scopus: 8
Solving the Software Project Scheduling Problem Using Intelligent Water Drops
(Univ Osijek, Tech Fac, 2018) Crawford, Broderick; Soto, Ricardo; Astorga, Gino; Castro, Carlos; Paredes, Fernando; Misra, Sanjay; Rubio, Jose-Miguel
Within the category of project scheduling problems, there is a specific problem within the software industry referred to as the software project scheduling problem. The problem consists in the correct allocation of employees to the different tasks that make up a software project, bearing in mind time and cost restraints. To achieve this goal, the present work first uses metaheuristic intelligent water drops illustrating; this is a recent stochastic swarm-based method increasingly used for solving optimization problems. Finally, the results and comparisons with experiments performed with other techniques are presented, demonstrating the solidity of the approach presented.
Citation - WoS: 4
Citation - Scopus: 6
A Teaching-Learning Optimization Algorithm for the Weighted Set-Covering Problem
(Univ Osijek, Tech Fac, 2020) Crawford, Broderick; Soto, Ricardo; Palma, Wenceslao; Aballay, Felipe; Astorga, Gino; Lemus-Romani, Jose; Rubio, Jose-Miguel
The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.