Benchmarking a hybrid multi level single linkagealgorithm on the bbob noiseless testbed

Authors:
László Pál
Affiliations:
Sapientia - Hungarian University of Transylvania, Miercurea-Ciuc, Romania
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

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Abstract

Multi Level Single Linkage (MLSL) is a well known stochastic global optimization method. In this paper, a new hybrid variant (HMLSL) of the MLSL algorithm is presented. The most important improvements are related to the sampling phase: the sample is generated from a Sobol quasi-random sequence and a few percent of the population is further improved by using crossover and mutation operators like in a traditional differential evolution (DE) method. The aim of this study is to evaluate the performance of the new HMLSL algorithm on the testbed of 24 noiseless functions. The new algorithm is also compared against a simple MLSL and a traditional DE in order to identify the benefits of the applied improvements. The results confirm that the HMLSL outperforms the MLSL and DE methods. The new method has a larger probability of success and usually is faster especially in the final stage of the optimization than the other two algorithms.