Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU144875" target="_blank" >RIV/00216305:26210/22:PU144875 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-09677-8_31" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-09677-8_31</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-09677-8_31" target="_blank" >10.1007/978-3-031-09677-8_31</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set

Popis výsledku v původním jazyce

In the field of evolutionary computation, benchmarking has a pivotal place in both the development of novel algorithms, and in performing comparisons between existing techniques. In this paper, the computational comparison of the Brain Storm Optimization (BSO) algorithm (a swarm intelligence paradigm inspired by the behaviors of the human process of brainstorming) was performed. A selected representative of the BSO algorithms (namely, BSO20) was compared with other selected methods, which were a mix of canonical methods (both swarm intelligence and evolutionary algorithms) and state-of-the-art techniques. As a test bed, the ambiguous benchmark set was employed. The results showed that even though BSO is not among the best algorithms on this test bed, it is still a well performing method comparable to some state-of-the-art algorithms.

Název v anglickém jazyce

Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set

Popis výsledku anglicky

In the field of evolutionary computation, benchmarking has a pivotal place in both the development of novel algorithms, and in performing comparisons between existing techniques. In this paper, the computational comparison of the Brain Storm Optimization (BSO) algorithm (a swarm intelligence paradigm inspired by the behaviors of the human process of brainstorming) was performed. A selected representative of the BSO algorithms (namely, BSO20) was compared with other selected methods, which were a mix of canonical methods (both swarm intelligence and evolutionary algorithms) and state-of-the-art techniques. As a test bed, the ambiguous benchmark set was employed. The results showed that even though BSO is not among the best algorithms on this test bed, it is still a well performing method comparable to some state-of-the-art algorithms.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Advances in Swarm Intelligence. ICSI 2022, Part II. Lecture Notes in Computer Science, vol 13344.

ISBN

978-3-031-09726-3

Počet stran výsledku

13

Strana od-do

367-379

Počet stran knihy

548

Název nakladatele

Springer, Cham

Místo vydání

Neuveden

Kód UT WoS kapitoly

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