Publikacje

@inproceedings{Trojanowski2020d,

title = {Variance of particle location in the stochastic model of PSO with inertia weight},

author = {Krzysztof Wójcik and Tomasz Kulpa and Krzysztof Trojanowski},

doi = { 10.1109/CEC48606.2020.9185567},

year  = {2020},

date = {2020-09-03},

booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)},

pages = {1-8},

publisher = {IEEE},

abstract = {In the Particle Swarm Optimization (PSO) method, the behavior of particles depends on movement parameters. Effective application of the PSO method in real-world problems requires stable behavior of particles in the swarm. In the stochastic particle stability analysis, recurrent formulas of expected value and variance of particle location are used. An explicit formula for the variance can also be obtained, however, it cannot be applied in practice due to its complexity. In our research, we propose assumptions guaranteeing a simple explicit formula for location variance. For the formula and given assumptions, we show stability areas in the particle configuration space, which guarantee order-2 * stability of particles also for probability distributions of movement parameters other than uniform. The areas are verified in simulations.},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro17_02,

title = {Particle Stability in PSO under Stagnation Assumption},

author = {Krzysztof Trojanowski and Tomasz Kulpa},

editor = {Christophe Sabourin, Juan Julian Merelo, Una-May O'Reilly, Kurosh Madani, Kevin Warwick},

url = {http://dx.doi.org/10.5220/0006501602730280},

doi = {10.5220/0006501602730280},

isbn = {978-989-758-274-5},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017)},

volume = {1},

pages = {273-280},

publisher = {SCITEPRESS},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro16_01,

title = {Particle Convergence Expected Time in The PSO Model with Inertia Weight},

author = {Krzysztof Trojanowski and Tomasz Kulpa},

url = {https://www.researchgate.net/publication/309835181_Particle_Convergence_Expected_Time_in_The_PSO_Model_with_Inertia_Weight},

doi = {10.5220/0006048700690077},

year  = {2016},

date = {2016-11-09},

booktitle = {Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016)},

volume = {1: ECTA},

pages = {69-77},

publisher = {SCITEPRESS},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro15_01,

title = {Particle Convergence Time in The PSO Model with Inertia Weight},

author = {Krzysztof Trojanowski and Tomasz Kulpa},

url = {http://dx.doi.org/10.5220/0005629701220130},

doi = {10.5220/0005629701220130},

isbn = {978-989-758-157-1},

year  = {2015},

date = {2015-11-12},

booktitle = {Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - Volume 1: ECTA},

pages = {122-130},

publisher = {SCITEPRESS},

abstract = {Particle Swarm Optimization (PSO) is a powerful heuristic optimization method being subject of continuous interest. Theoretical analysis of its properties concerns primarily the conditions necessary for guaranteeing its convergent behaviour. Particle behaviour depends on three groups of parameters: values of factors in a velocity update rule, initial localization and velocity and fitness landscape. The paper presents theoretical analysis of the particle convergence properties in the model with inertia weight respectively to different values of these parameters. A new measure for evaluation of a particle convergence time is proposed. For this measure an upper bound formula is derived and its four main types of characteristics are discussed. The way of the characteristics transformations respectively to changes of velocity equation parameters is presented as well.},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro08_03,

title = {Adaptive Non-uniform Distribution of Quantum Particles in mQSO},

author = {Krzysztof Trojanowski},

year  = {2008},

date = {2008-01-01},

booktitle = {SEAL 2008},

volume = {5361},

pages = {91-100},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro08_04,

title = {Multi-Swarm That Learns},

author = {Krzysztof Trojanowski},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

booktitle = {Intelligent Information Systems XVI},

pages = {121-130},

publisher = {Akademicka Oficyna Wydawnicza EXIT},

series = {Challenging Problems of Science},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro08_02,

title = {Non-uniform Distributions of Quantum Particles in Multi-swarm Optimization for Dynamic Tasks},

author = {Krzysztof Trojanowski},

year  = {2008},

date = {2008-01-01},

booktitle = {ICCS 2008, Part I},

volume = {5101},

pages = {843-852},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tro08_01,

title = {Tuning Quantum Multi-Swarm Optimization for Dynamic Tasks},

author = {Krzysztof Trojanowski},

year  = {2008},

date = {2008-01-01},

booktitle = {ICAISC 2008},

volume = {5097},

pages = {499-510},

publisher = {Springer},

series = {Lecture Notes in Artificial Intelligence},

keywords = {PSO},

pubstate = {published},

tppubtype = {inproceedings}

}