Abstract
This paper describes a novel initialization for Deterministic Particle Swarm Optimization (DPSO), based on choosing specific dense initial positions and velocities for particles. This choice tends to induce orthogonality of particles’ trajectories, in the early iterations, in order to better explore the search space. Our proposal represents an improvement, by the same authors, of the theoretical analysis on a previously proposed PSO reformulation, namely the initialization ORTHOinit. A preliminary experience on constrained Portfolio Selection problems confirms our expectations.
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Acknowledgments
The research is partially supported by the Italian Flagship Project RITMARE, coordinated by the Italian National Research Council and funded by the Italian Ministry of Education, University and Research. Matteo Diez is grateful to Dr Woei-Min Lin and Dr Ki-Han Kim of the US Navy Office of Naval Research, for their support through NICOP grant N62909-15-1-2016.
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Diez, M., Serani, A., Leotardi, C., Campana, E.F., Fasano, G., Gusso, R. (2016). Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit+ . In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_32
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DOI: https://doi.org/10.1007/978-3-319-41000-5_32
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