@inproceedings{1e903a4c42d04f84a7286dbea76cd760,
title = "Exponential inertia weight for particle swarm optimization",
abstract = "The exponential inertia weight is proposed in this work aiming to improve the search quality of Particle Swarm Optimization (PSO) algorithm. This idea is based on the adaptive crossover rate used in Differential Evolution (DE) algorithm. The same formula is adopted and applied to inertia weight, w. We further investigate the characteristics of the adaptive w graphically and careful analysis showed that there exists two important parameters in the equation for adaptive w; one acting as the local attractor and the other as the global attractor. The 23 benchmark problems are adopted as test bed in this study; consisting of both high and low dimensional problems. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is used widely in literature.",
keywords = "Benchmark functions, Particle Swarm Optimization, exponential inertia weight",
author = "Ting, {T. O.} and Yuhui Shi and Shi Cheng and Sanghyuk Lee",
year = "2012",
doi = "10.1007/978-3-642-30976-2_10",
language = "English",
isbn = "9783642309755",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "83--90",
booktitle = "Advances in Swarm Intelligence - Third International Conference, ICSI 2012, Proceedings",
edition = "PART 1",
note = "3rd International Conference on Swarm Intelligence, ICSI 2012 ; Conference date: 17-06-2012 Through 20-06-2012",
}