<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper.</p><p>We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.</p></abstract>

This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper.We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.

Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

Faculty of Mathematics and Information Science

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a...

Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

Data publikacji: 18 wrz 2020

Zakres stron: 233 - 254

Otrzymano: 28 lut 2020

Przyjęty: 22 cze 2020

DOI: https://doi.org/10.2478/fcds-2020-0013

Słowa kluczowe
Particle Swarm Optimization, global optimization, metaheuristic

© 2020 Mateusz Zaborski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

Data publikacji: 18 wrz 2020

Zakres stron: 233 - 254

Otrzymano: 28 lut 2020

Przyjęty: 22 cze 2020

DOI: https://doi.org/10.2478/fcds-2020-0013

Słowa kluczoweParticle Swarm Optimization, global optimization, metaheuristic

© 2020 Mateusz Zaborski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Particle Swarm Optimization, global optimization, metaheuristic