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Strategies for balancing exploration and exploitation in electromagnetic optimisation

Strategies for balancing exploration and exploitation in electromagnetic optimisation
Strategies for balancing exploration and exploitation in electromagnetic optimisation
Purpose – Electromagnetic design utilising finite element or similar numerical methods is computationally expensive, thus efficient algorithms reducing the number of objective function calls to locate the optimum are sought. The balance between exploration and exploitation may be achieved using a reinforcement learning approach, as demonstrated previously. However, in practical design problems, in addition to finding the global optimum efficiently, information about the robustness of the solution may also be important. In this paper, the aim is to discuss the suitability of different search algorithms and to present their fitness to solve the optimization problem in conjunction with providing enough information on the robustness of the solution.
Design/methodology/approach – Two novel strategies enhanced by the surrogate model based weighted expected improvement approach are discussed. The algorithms are tested using a two-variable test function. The emphasis of these strategies is on accurate approximation of the shape of the objective function to accomplish a robust design.
Findings – The two novel strategies aim to pursue the optimal value of weights for exploration and exploitation throughout the iterative process for better prediction of the shape of the objective function.
Originality/value – It is argued that the proposed strategies based on adaptively tuning weights perform better in predicting the shape of the objective function. Good accuracy of predicting the shape of the objective function is crucial for achieving a robust design.
0332-1649
1176-1188
Xiao, Song
6ffa9657-513e-4b86-86a2-e560d3c09c72
Rotaru, M.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, J. K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Xiao, Song
6ffa9657-513e-4b86-86a2-e560d3c09c72
Rotaru, M.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, J. K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb

Xiao, Song, Rotaru, M. and Sykulski, J. K. (2013) Strategies for balancing exploration and exploitation in electromagnetic optimisation. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 32 (4), 1176-1188. (doi:10.1108/03321641311317004).

Record type: Article

Abstract

Purpose – Electromagnetic design utilising finite element or similar numerical methods is computationally expensive, thus efficient algorithms reducing the number of objective function calls to locate the optimum are sought. The balance between exploration and exploitation may be achieved using a reinforcement learning approach, as demonstrated previously. However, in practical design problems, in addition to finding the global optimum efficiently, information about the robustness of the solution may also be important. In this paper, the aim is to discuss the suitability of different search algorithms and to present their fitness to solve the optimization problem in conjunction with providing enough information on the robustness of the solution.
Design/methodology/approach – Two novel strategies enhanced by the surrogate model based weighted expected improvement approach are discussed. The algorithms are tested using a two-variable test function. The emphasis of these strategies is on accurate approximation of the shape of the objective function to accomplish a robust design.
Findings – The two novel strategies aim to pursue the optimal value of weights for exploration and exploitation throughout the iterative process for better prediction of the shape of the objective function.
Originality/value – It is argued that the proposed strategies based on adaptively tuning weights perform better in predicting the shape of the objective function. Good accuracy of predicting the shape of the objective function is crucial for achieving a robust design.

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Published date: July 2013
Organisations: EEE

Identifiers

Local EPrints ID: 355123
URI: http://eprints.soton.ac.uk/id/eprint/355123
ISSN: 0332-1649
PURE UUID: 4fb29938-8bff-4f0f-8678-1a79f633e378
ORCID for J. K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

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Date deposited: 30 Jul 2013 11:53
Last modified: 22 Oct 2019 00:58

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