Phasor Prticle Swarm Optimization (PPSO)

버전 1.0.2 (4.99 KB) 작성자: Ebrahim Akbari

Phasor Prticle Swarm Optimization (PPSO) inspired from phasor theory in the mathematics.

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업데이트 날짜: 2021/10/19

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Phasor particle swarm optimization (PPSO) is a new and primary sample of PSO, based on modeling the particle control parameters with a phase angle (θ), inspired from phasor theory in the mathematics. This phase angle (θ) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions.

Based on the paper:

Mojtaba Ghasemi, Ebrahim Akbari, Abolfazl Rahimnejad, Seyed Ehsan Razavi, Sahand Ghavidel, and Li Li. "Phasor particle swarm optimization: a simple and efficient variant of PSO." Soft Computing 23, no. 19 (2019): 9701-9718. DOI: https://doi.org/10.1007/s00500-018-3536-8

The full-text view-only version of the paper is available at: https://rdcu.be/720D

인용 양식

Ebrahim Akbari (2024). Phasor Prticle Swarm Optimization (PPSO) (https://www.mathworks.com/matlabcentral/fileexchange/75873-phasor-prticle-swarm-optimization-ppso), MATLAB Central File Exchange. 검색됨 2024/4/25.

Ghasemi, Mojtaba, et al. Phasor Particle Swarm Optimization: a Simple and Efficient Variant of PSO. no. 19, Springer Science and Business Media LLC, Sept. 2018, pp. 9701–18, doi:10.1007/s00500-018-3536-8.

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개발 환경: R2018b

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PPSO MATLAB Codes

버전	게시됨	릴리스 정보
1.0.2	2021/10/19	Editing description.	다운로드
1.0.1	2021/10/19	Adding DOI to the reference paper.	다운로드
1.0.0	2020/5/23		다운로드

MLA	Ghasemi, Mojtaba, et al. Phasor Particle Swarm Optimization: a Simple and Efficient Variant of PSO. no. 19, Springer Science and Business Media LLC, Sept. 2018, pp. 9701–18, doi:10.1007/s00500-018-3536-8.
APA	Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2018). Phasor particle swarm optimization: a simple and efficient variant of PSO, 23(19), 9701–9718. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs00500-018-3536-8
BibTeX	@article{2018, doi = {10.1007/s00500-018-3536-8}, url = {https://doi.org/10.1007%2Fs00500-018-3536-8}, year = 2018, month = {sep}, publisher = {Springer Science and Business Media {LLC}}, volume = {23}, number = {19}, pages = {9701--9718}, author = {Mojtaba Ghasemi and Ebrahim Akbari and Abolfazl Rahimnejad and Seyed Ehsan Razavi and Sahand Ghavidel and Li Li}, title = {Phasor particle swarm optimization: a simple and efficient variant of {PSO}} }