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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

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20232

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Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    Hierarchical Distributed Cluster-based Method for Robotic Swarms
    (Wydawnictwo Politechniki Łódzkiej, 2023) Mastej, Bartłomiej; Figat, Maksym
    The growing interest in autonomous systems inspired by nature has led to a major shift towards swarm robotics. The main characteristics of swarms are independence from global knowledge, scalability and relatively low cost. However, the design of a swarm system is still a challenging task. Most of the existing research focuses on the task-specific solutions, which are hardly applicable to other solutions. Therefore, in this paper we present the method that provides a general guideline for the design of the swarm systems. The approach is verified in the simulation of the letter formation task.
  • Pozycja
    A Novel Learning Multi-Swarm Particle Swarm Optimization
    (Wydawnictwo Politechniki Łódzkiej, 2023) Borowska, Bożena
    Particle swarm optimization (PSO) is one of the metaheuristic optimization methods. Because of its many advantages, it is often used to solve real-world engineering problems. However, in case of complex, multidimensional tasks, PSO faces some problems related to premature convergence and stagnation in local optima. To eliminate this inconveniences, in this paper, a new learning multi-swarm particle swarm optimization method (LMPSO) with local search operator has been proposed. The presented approach was tested on a set of nonlinear functions and a CEC2015 test suite. The obtained results were compared with other optimization methods.