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INFORMATICA, 2016, Vol. 27, No. 2, 299-322
© Institute of Mathematics and Informatics,
DOI: http://dx.doi.org/10.15388/Informatica.2016.86

ISSN 0868-4952

CoRSO (Collaborative Reactive Search Optimization): Blending Combinatorial and Continuous Local Search

Mauro BRUNATO, Roberto BATTITI1

Department of Information Engineering and Computer Science, University of Trento, Italy, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia E-mail: mauro.brunato@unitn.it, roberto.battiti@unitn.it

Abstract

We propose a heuristic global optimization technique which combines combinatorial and continuous local search. The combinatorial component, based on Reactive Search Optimization, generates a trajectory of binary strings describing search districts. Each district is evaluated by random sampling and by selective runs of continuous local search. A reactive prohibition mechanisms guarantees that the search is not stuck at locally optimal districts.

The continuous stochastic local search is based on the Inertial Shaker method: candidate points are generated in an adaptive search box and a moving average of the steps filters out evaluation noise and high-frequency oscillations.

The overall subdivision of the input space in a tree of non-overlapping search districts is adaptive, with a finer subdivision in the more interesting input zones, potentially leading to lower local minima.

Finally, a portfolio of independent CoRSO search streams (P-CoRSO) is proposed to increase the robustness of the algorithm.

An extensive experimental comparison with Genetic Algorithms and Particle Swarm demonstrates that CoRSO and P-CoRSO reach results which are fully competitive and in some cases significantly more robust.

Keywords:

global optimization, reactive search optimization, algorithm portfolios


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