Optimizing parameters in swarm intelligence using reinforcement learning: An application of Proximal Policy Optimization to the iSOMA algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F24%3A10254282" target="_blank" >RIV/61989100:27740/24:10254282 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/24:10254282 RIV/61989100:27730/24:10254282

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2210650224000208" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650224000208</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2024.101487" target="_blank" >10.1016/j.swevo.2024.101487</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing parameters in swarm intelligence using reinforcement learning: An application of Proximal Policy Optimization to the iSOMA algorithm

Popis výsledku v původním jazyce

This paper presents a new algorithm for optimizing parameters in swarm algorithm using reinforcement learning. The algorithm, called iSOMA-RL, is based on the iSOMA algorithm, a population-based optimization algorithm that mimics the competition-cooperation behavior of creatures to find the optimal solution. By using reinforcement learning, iSOMA-RL can dynamically and continuously optimize parameters, which can play a crucial role in determining the performance of the algorithm but are often difficult to determine. The reinforcement learning technique used is the state-of-the-art Proximal Policy Optimization (PPO), which has been successful in many areas. The algorithm was compared to the original iSOMA algorithm and other algorithms from the SOMA family, showing better performance with only constant increase in computational complexity depending on number of function evaluations. Also we examine different sets of parameters to optimize and different reward functions. We also did comparison to widely used and state-of-the-art algorithms to illustrate improvement in performance over the original iSOMA algorithm. (C) 2024 The Authors

Název v anglickém jazyce

Optimizing parameters in swarm intelligence using reinforcement learning: An application of Proximal Policy Optimization to the iSOMA algorithm

Popis výsledku anglicky

This paper presents a new algorithm for optimizing parameters in swarm algorithm using reinforcement learning. The algorithm, called iSOMA-RL, is based on the iSOMA algorithm, a population-based optimization algorithm that mimics the competition-cooperation behavior of creatures to find the optimal solution. By using reinforcement learning, iSOMA-RL can dynamically and continuously optimize parameters, which can play a crucial role in determining the performance of the algorithm but are often difficult to determine. The reinforcement learning technique used is the state-of-the-art Proximal Policy Optimization (PPO), which has been successful in many areas. The algorithm was compared to the original iSOMA algorithm and other algorithms from the SOMA family, showing better performance with only constant increase in computational complexity depending on number of function evaluations. Also we examine different sets of parameters to optimize and different reward functions. We also did comparison to widely used and state-of-the-art algorithms to illustrate improvement in performance over the original iSOMA algorithm. (C) 2024 The Authors

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

—

Svazek periodika

85

Číslo periodika v rámci svazku

101487

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

nestránkováno

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85183455296