Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

Authors: Rostami, S. and Ferrante, N.

http://eprints.bournemouth.ac.uk/24371/

Journal: Integrated Computer-Aided Engineering

Volume: 23

Issue: 4

Pages: 313-329

ISSN: 1069-2509

DOI: 10.3233/ICA-160529

Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA-PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic, representing the state-of-the-art in this sub-field of multi-objective optimisation. The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.

This source preferred by Shahin Rostami

This data was imported from Scopus:

Authors: Rostami, S. and Neri, F.

http://eprints.bournemouth.ac.uk/24371/

Journal: Integrated Computer-Aided Engineering

Volume: 23

Issue: 4

Pages: 313-329

eISSN: 1875-8835

ISSN: 1069-2509

DOI: 10.3233/ICA-160529

© 2016 - IOS Press and the author(s). All rights reserved. Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolumesorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA-PAES in handling more than two ob jectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic, representing the state-of-the-art in this sub-field of multi-objective optimisation. The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.

This data was imported from Web of Science (Lite):

Authors: Rostami, S. and Neri, F.

http://eprints.bournemouth.ac.uk/24371/

Journal: INTEGRATED COMPUTER-AIDED ENGINEERING

Volume: 23

Issue: 4

Pages: 313-329

eISSN: 1875-8835

ISSN: 1069-2509

DOI: 10.3233/ICA-160529

The data on this page was last updated at 04:43 on November 23, 2017.