A Convergence Acceleration Operator for Multi-Objective Optimisation
Adra, S.F., Griffin, I.A. and Fleming, P.J.
(2007)
A Convergence Acceleration Operator for Multi-Objective Optimisation.
Research Report.
ACSE Research Report 950
.
Department of Automatic Control and Systems Engineering
Abstract
A novel multi-objective optimisation accelerator is introduced that uses direct manipulation in objective space together with neural network mappings from objective space to decision space. This operator is a portable component that can be hybridized with any multi-objective optimisation algorithm. The purpose of this Convergence Accelerator Operator (CAO) is to enhance the search capability and the speed of convergence of the host algorithm. The operator acts directly in objective space to suggest improvements to solutions obtained by a multi-objective evolutionary algorithm (MOEA). These suggested improved objective vectors are then mapped into decision variable space and tested, The CAO is incorporated with two leading MOEA's, the Non-Dominated Sorting Genetic Algorithm (NSGA-II and the Strength Pareto Evolutionary Algorithm (SPEA2) and tested. Results show that the hybridized algorithms consistently improve the speed of convergence of the original algorithm whilst maintaining the desired distribution of solutions.
Metadata
Item Type:
Monograph
Authors/Creators:
Adra, S.F.
Griffin, I.A.
Fleming, P.J.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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