Fuzzy Bi-Objective Particle Swarm Optimization for Next Release Problem (S) | Zendy

Carlos A. Casanova | Zendy; Giovanni Daián Rottoli | Zendy; Esteban Alejandro Schab | Zendy; Luciano Joaquín Bracco | Zendy; Fernando Pereyra Rausch | Zendy; Anabella De Battista | Zendy

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Fuzzy Bi-Objective Particle Swarm Optimization for Next Release Problem (S)

Author(s) -

Carlos A. Casanova,

Giovanni Daián Rottoli,

Esteban Alejandro Schab,

Luciano Joaquín Bracco,

Fernando Pereyra Rausch,

Anabella De Battista

Publication year - 2019

Publication title -

proceedings/proceedings of the ... international conference on software engineering and knowledge engineering

Language(s) - English

Resource type - Conference proceedings

eISSN - 2325-9000

pISSN - 2325-9086

DOI - 10.18293/seke2019-082

Subject(s) - particle swarm optimization , computer science , fuzzy logic , mathematical optimization , multi swarm optimization , metaheuristic , particle (ecology) , artificial intelligence , mathematics , algorithm , geology , oceanography

In search-based software engineering (SBSE), software engineers usually have to select one among many quasi-optimal solutions with different values for the objectives of interest for a particular problem domain. Because of this, a metaheuristic algorithm is needed to explore a larger extension of the Pareto optimal front to provide a bigger set of possible solutions. In this regard the Fuzzy Multi-Objective Particle Swarm Optimization (FMOPSO), a novel a posteriori algorithm, is proposed in this paper and compared with other state-of-the-art algorithms. The results show that FMOPSO is adequate for finding very detailed Pareto Fronts.

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