4 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
Anarchy in the swarm: Testing informed and uninformed diversity-enhancing mechanisms within PSO framework
Piotr Urbańczyk, Aleksandra Urbańczyk
Particle Swarm Optimization (PSO) frequently suffers from premature convergence. This paper introduces a family of problem-informed diversity-enhancing strategies that manipulate t…
TOPSIS-like metaheuristic for LABS problem
Aleksandra Urbańczyk, Bogumiła Papiernik, Piotr Magiera +2
This paper presents the application of socio-cognitive mutation operators inspired by the TOPSIS method to the Low Autocorrelation Binary Sequence (LABS) problem. Traditional evolu…
Socio-cognitive agent-oriented evolutionary algorithm with trust-based optimization
Aleksandra Urbańczyk, Krzysztof Czech, Piotr Urbańczyk +2
This paper introduces the Trust-Based Optimization (TBO), a novel extension of the island model in evolutionary computation that replaces conventional periodic migrations with a fl…
Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics
Piotr Urbańczyk, Aleksandra Urbańczyk, Magdalena Król +2
The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive man…
Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics
Piotr Kipinski, Hubert Guzowski, Aleksandra Urbanczyk +8
Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing nov…