2 research outputs found
Improving LSHADE by means of a pre-screening mechanism
Evolutionary algorithms have proven to be highly effective in continuous
optimization, especially when numerous fitness function evaluations (FFEs) are
possible. In certain cases, however, an expensive optimization approach (i.e.
with relatively low number of FFEs) must be taken, and such a setting is
considered in this work. The paper introduces an extension to the well-known
LSHADE algorithm in the form of a pre-screening mechanism (psLSHADE). The
proposed pre-screening relies on the three following components: a specific
initial sampling procedure, an archive of samples, and a global linear
meta-model of a fitness function that consists of 6 independent transformations
of variables. The pre-screening mechanism preliminary assesses the trial
vectors and designates the best one of them for further evaluation with the
fitness function. The performance of psLSHADE is evaluated using the CEC2021
benchmark in an expensive scenario with an optimization budget of 10^2-10^4
FFEs per dimension. We compare psLSHADE with the baseline LSHADE method and the
MadDE algorithm. The results indicate that with restricted optimization budgets
psLSHADE visibly outperforms both competitive algorithms. In addition, the use
of the pre-screening mechanism results in faster population convergence of
psLSHADE compared to LSHADE.Comment: Accepted at Genetic and Evolutionary Computation Conference
(GECCO'22
Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples
In this paper we enhance Generalized Self-Adapting Particle Swarm
Optimization algorithm (GAPSO), initially introduced at the Parallel Problem
Solving from Nature 2018 conference, and to investigate its properties. The
research on GAPSO is underlined by the two following assumptions: (1) it is
possible to achieve good performance of an optimization algorithm through
utilization of all of the gathered samples, (2) the best performance can be
accomplished by means of a combination of specialized sampling behaviors
(Particle Swarm Optimization, Differential Evolution, and locally fitted square
functions). From a software engineering point of view, GAPSO considers a
standard Particle Swarm Optimization algorithm as an ideal starting point for
creating a generalpurpose global optimization framework. Within this framework
hybrid optimization algorithms are developed, and various additional techniques
(like algorithm restart management or adaptation schemes) are tested. The paper
introduces a new version of the algorithm, abbreviated as M-GAPSO. In
comparison with the original GAPSO formulation it includes the following four
features: a global restart management scheme, samples gathering within an
R-Tree based index (archive/memory of samples), adaptation of a sampling
behavior based on a global particle performance, and a specific approach to
local search. The above-mentioned enhancements resulted in improved performance
of M-GAPSO over GAPSO, observed on both COCO BBOB testbed and in the black-box
optimization competition BBComp. Also, for lower dimensionality functions (up
to 5D) results of M-GAPSO are better or comparable to the state-of-the art
version of CMA-ES (namely the KL-BIPOP-CMA-ES algorithm presented at the GECCO
2017 conference).Comment: preprin