CEC 2021 Tutorial — Onepage Template by Colorlib

How well do you know the parameter space of PSO?

Particle Swarm Optimization applied to the Ackley function

Attend the tutorial to understand why the landscape looks this way, as well as many aspects of PSO that will improve both you understanding and real world use of PSO!

How well do you know the parameter space of PSO?

Particle Swarm Optimization applied to the Egg Holder function

Attend the tutorial to understand why the landscape looks this way, as well as many aspects of PSO that will improve both you understanding and real world use of PSO!

How well do you know the parameter space?

Particle Swarm Optimization applied to the Spherical function

Attend the tutorial to understand why the landscape looks this way, as well as many aspects of PSO that will improve both you understanding and real world use of PSO!!

How well do you know the parameter space?

Particle Swarm Optimization applied to the Rastrigin function

Attend the tutorial to understand why the landscape looks this way, as well as many aspects of PSO that will improve both you understanding and real world use of PSO!

Advances in Particle Swarm Optimization Development, Analysis, and Understanding

The main objective of this tutorial will be to inform particle swarm optimization (PSO) practitioners of the many common misconceptions and falsehoods that are actively hindering a practitioner’s successful use of PSO in solving challenging optimization problems. While the behaviour of PSO’s particles has been studied both theoretically and empirically since its inception in 1995, most practitioners unfortunately have not utilized these studies to guide their use of PSO. This tutorial will provide a succinct coverage of common PSO misconceptions, with a detailed explanation of why the misconceptions are in fact false, and how they are negatively impacting results. The tutorial will also provide recent theoretical results about PSO particle behaviour from which the PSO practitioner can now make better and more informed decisions about PSO and in particular make better PSO parameter selections. The tutorial will focus on the following important aspects of PSO behaviour

Understanding why the random variables used in the velocity update should not be scalars, but rather vectors of random values
Exploring the effects of different ways in which velocity can be initialized
The influence of social topology and different iteration strategies on performance is discussed
Understanding PSO control parameters, and how to use them more efficiently

The importance of parameter selection will be illustrated with an interactive demo where audience members will vote for/suggest control parameters. From the set of audience selected control parameters relative performance ranking, based on popular benchmark suites, of these configuration will be given in relation to a very extensive set of possible configurations. This demo will clearly illustrate why the subsequent theoretical discussion on control parameters is so import for effective PSO use

Existing theoretical PSO results and what they mean to a PSO practitioner
Roaming behaviour of PSO particles
Understanding why PSO struggles with large-scale optimization problems
Known stability criteria of PSO algorithms
Effects of particle stability of PSO’s performance
How to derive new stability criteria for PSO variants and verify them

The use of a general PSO stability theorem in addition to a simple to use specialization are demonstrated
The newly derived specialization theorem allows for a non-restrictive relationship of PSO control coefficients, which is a first for PSO stability theory

Control parameter tuning, and self-adaptive control parameters
Is the PSO a local optimizer or a global optimizer?

With the knowledge presented in this tutorial a PSO practitioner will gain up to date theoretical insights into PSO behaviour and as a result be able to make informed choices when utilizing PSO.

Presenters

Christopher Cleghorn received his Masters and PhD degrees in Computer Science from the University of Pretoria, South Africa, in 2013 and 2017 respectively. He is an Associate Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand. His research interests include swarm intelligence, evolutionary computation, machine learning, and radio-astronomy with a strong focus of theoretical research. Prof Cleghorn annually serves as a reviewer for numerous international journals and conferences in domains ranging from swarm intelligence and neural networks to mathematical optimization.

Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. His research interests include swarm intelligence, evolutionary computation, artificial neural networks, artificial immune systems, and the application of these Computational Intelligence paradigms to data analytics, games, bioinformatics, finance, and difficult optimization problems. He is author of two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence. Prof Engelbrecht serves as an associate-editor for a number of journals, and serves as a reviewer for numerous journals and conferences.