(μ +λ) Evolution Strategy with Socio-Cognitive Mutation

Aleksandra Urbanczyk; Krzysztof Kucaba; Mateusz Wojtulewicz; Marek Kisiel-Dorohinicki; Leszek Rutkowski; Piotr Duda; Janusz Kacprzyk; Xin Yao; Siang Yew Chong; Aleksander Byrski

doi:10.14313/jamris/1-2024/1

(μ +λ) Evolution Strategy with Socio-Cognitive Mutation

Aleksandra Urbanczyk
, Krzysztof Kucaba
, Mateusz Wojtulewicz
, Marek Kisiel-Dorohinicki
, Leszek Rutkowski
, Piotr Duda
, Janusz Kacprzyk
, Xin Yao
, Siang Yew Chong
, Aleksander Byrski

Research output: Journal Publication › Article › peer-review

3 Citations (Scopus)

Abstract

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

Original language	English
Pages (from-to)	1-11
Number of pages	11
Journal	Journal of Automation, Mobile Robotics and Intelligent Systems
Volume	18
Issue number	1
DOIs	https://doi.org/10.14313/jamris/1-2024/1
Publication status	Published - 1 Mar 2024
Externally published	Yes

Free Keywords

global optimization
metaheuristics
socio-cognitive computing

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Artificial Intelligence

Access to Document

10.14313/jamris/1-2024/1

Cite this

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abstract = "Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.",

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AU - Urbanczyk, Aleksandra

AU - Kucaba, Krzysztof

AU - Wojtulewicz, Mateusz

AU - Kisiel-Dorohinicki, Marek

AU - Rutkowski, Leszek

AU - Duda, Piotr

AU - Kacprzyk, Janusz

AU - Yao, Xin

AU - Chong, Siang Yew

AU - Byrski, Aleksander

PY - 2024/3/1

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AB - Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

KW - global optimization

KW - metaheuristics

KW - socio-cognitive computing

UR - https://www.scopus.com/pages/publications/85192020816

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DO - 10.14313/jamris/1-2024/1

M3 - Article

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JF - Journal of Automation, Mobile Robotics and Intelligent Systems

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ER -

(μ +λ) Evolution Strategy with Socio-Cognitive Mutation

Abstract

Free Keywords

ASJC Scopus subject areas

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