1 research outputs found
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