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Particle Swarm Methods

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Abstract

Particle swarm optimization has gained increasing popularity in the past 15 years. Its effectiveness and efficiency has rendered it a valuable metaheuristic approach in various scientific fields where complex optimization problems appear. Its simplicity has made it accessible to the non-expert researchers, while the potential for easy adaptation of operators and integration of new procedures allows its application on a wide variety of problems with diverse characteristics. Additionally, its inherent decentralized nature allows easy parallelization, taking advantage of modern high-performance computer systems. The present work exposes the basic concepts of particle swarm optimization and presents a number of popular variants that opened new research directions by introducing novel ideas in the original model of the algorithm. The focus is placed on presenting the essential information of the algorithms rather than covering all the details. Also, a large number of references and sources is provided for further inquiry. Thus, the present text can serve as a starting point for researchers interested in the development and application of particle swarm optimization and its variants.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece

    Konstantinos E. Parsopoulos

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  1. Konstantinos E. Parsopoulos
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Correspondence to Konstantinos E. Parsopoulos .

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  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Parsopoulos, K.E. (2018). Particle Swarm Methods. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_22

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