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Particle Swarm Optimization

Andries Engelbrecht
Professor Computer Science
University of Pretoria, South Africa


Since the first publication of Particle Swarm Optimization (PSO) in 1995, the number of research papers on PSO, and the number of researcher in PSO, have exploded. Many variations of the PSO have been developed to improve its performance, studies have been done to understand the dynamics of particles, and adaptations have been developed to apply the PSO to different optimization problem types. This tutorial is planned in two parts, one introductory, and one addressing more advanced topics. The introductory part will have as its objective to provide the attendee with an overview of PSO, its behaviors, and its basic variations. Issues with the standard PSO algorithm will be pointed out. The advanced part of the tutorial will consider PSO models for solving difficult optimization problems, and will also provide a review of theoretical analyses of PSO.

In more detail, the tutorial will cover the following topics, in the order listed below:


1. Basic PSO: The philosophy of PSO will be discussed, and the basic (original) PSO algorithms will be explained and illustrated. The need for social network structures will be discussed, as well as the importance of PSO control parameters, basic variations (velocity clamping, inertia, constriction). Issues with the original PSO will be discussed (including the roaming behavior of particles, how velocities shoul dbe initialized). The behavior of particles under different conditions will be discussed. An overview of performance criteria will be given.
2. Single-solution PSO: A number of variations of the basic PSO to locate a single solution will be presented and illustrated. This will include approaches that use multiple swarms, hybrids with evolutionary algorithms, and many more.


3. Particle trajectories: The behavior of PSO particles will be discussed, and formal heuristics derived to guide selection of values for control parameters. Example particle trajectories will be illustrated, and one of the major pitfalls of PSO will be pointed out and corrected.
4. Niching PSO: It will be shown how the PSO can be adapted to find multiple solutions.
5. MOO PSO: This section will show how PSO can be used to solve multiobjective optimization problems.
6. Constrained PSO: Here PSO variations to solve constrained problems will be discussed.
7. Dynamic environments: It will be shown how PSO can be adapted to maintain and track single and multiple optima in dynamically changing environments.
8. Discrete optimization: Changes to PSO to solve binary/discrete-valued problems will be discussed and illustrated.



Andries Engelbrecht is a Full Professor in Computer Science at the Department of Computer Science, University of Pretoria,and South African Research Chair in AI. He manages a research group of 40 Masters and PhD students, most of whom do research in swarm intelligence. He has recently authored a book, “Fundamentals of Computational Swarm Intelligence”, published by Wiley. He is also the author of a book, “Computational Intelligence: An Introduction”, also published by Wiley. He has presented tutorials on PSO and Coevolutionary methods for evolving game agents at IEEE CEC 2005 and IEEE CIG 2005 respectively. He is copresenter of a tutorial on PSO and DE at IEEE CEC 2007, and PSO at GECCO 2007. He also presented PSO tutorials at ACISS and ACAL 2010, and to a number of universities. He has published approximately 200 papers in the last decade, serves as a reviewer for a number of conferences and journals, and is an associate-editor of IEEE TEC, IEEE TCIAIG, ans Swarm Intelligence, and serves on the editorial board of three other journals. He served as a member of a large number of conference program committees, and is in the organizing committee of several conferences.


The length of the tutorial:
two hours.


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