Multi-objective particle swarm optimisation for molecular transition state search

Jan Hettenhausen*, Andrew Lewis, Stephen Chen, Marcus Randall, Rene Fournier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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This paper describes a novel problem formulation and specialised Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm to discover the reaction pathway and Transition State (TS) of small molecules. Transition states play an important role in computational chemistry and their discovery represents one of the big challenges in computational chemistry. This paper presents a novel problem formulation that defines the TS search as a multi-objective optimisation (MOO) problem. A proof of concept of a modified multi-objective particle swarm optimisation algorithm is presented to find solutions to this problem. While still at a prototype stage, the algorithm was able to find solutions in proximity to the actual TS in many cases. The algorithm is demonstrated on a range of molecules with qualitatively different reaction pathways. Based on this evaluation, possible future developments will be discussed.

Original languageEnglish
Title of host publicationEvolve: A bridge between probability, set oriented numerics, and evolutionary computation II
Subtitle of host publicationAdvances in intelligent systems and computing
EditorsO Schutze, CAC Coello, AA Tantar, E Tantar, P Bouvry, P DelMoral, P Legrand
Number of pages16
ISBN (Print)978-3-642-31518-3
Publication statusPublished - 2012
Externally publishedYes
EventEVOLVE 2012 International Conference: A bridge between Probability, Set Oriented Numerics and Evolutionary Computation - Mexico City, Mexico City, Mexico
Duration: 7 Aug 20129 Aug 2012

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


ConferenceEVOLVE 2012 International Conference
CityMexico City
Internet address


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