The benefits of adaptive parametrization in multi-objective Tabu Search optimization

Journal article (2010)

Authors

Tiziano Ghisu University of Cambridge
Geoffrey T. Parks University of Cambridge
Daniel M. Jaeggi University of Cambridge
Jerome P. Jarrett University of Cambridge
P.J. Clarkson University of Cambridge
Affiliation
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Multi-objective optimization Adaptive parametrization Principal components' analysis Tabu Search
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Published Date

2010-10

Language

English

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Abstract

In real-world optimization problems, large design spaces and conflicting objectives are often combined with a large number of constraints, resulting in a highly multi-modal, challenging, fragmented landscape. The local search at the heart of Tabu Search, while being one of its strengths in highly constrained optimization problems, requires a large number of evaluations per optimization step. In this work, a modification of the pattern search algorithm is proposed: this modification, based on a Principal Components' Analysis of the approximation set, allows both a re-alignment of the search directions, thereby creating a more effective parametrization, and also an informed reduction of the size of the design space itself. These changes make the optimization process more computationally efficient and more effective - higher quality solutions are identified in fewer iterations. These advantages are demonstrated on a number of standard analytical test functions (from the ZDT and DTLZ families) and on a real-world problem (the optimization of an axial compressor preliminary design).