Browsing by Subject "swarm intelligence"

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Access status: Open Access ,
An adaptive framework for mobile robot navigation
(2017) Güzel, Mehmet Serdar; Kara, Mehmet; Beyazkılıç, Mehmet Sıtkı
Collective behaviours observed in nature bring new methodologies in proposing control algorithms for robot groups to perform a variety of complex tasks. In this article, an adaptive algorithm, allowing the safe navigation of a group of robots in a collective manner, is proposed. The algorithm, inspired from the adaptive particle swarm optimization technique, proposes an efficient control approach to overcome both static and moving obstacles. Accordingly, compared to the conventional particle swarm optimization algorithm, the proposed system allows a robot or group of robots (swarm) to complete the goal while avoiding static and moving obstacles as well as dynamic targets in a safe and collective manner. The simulation results verify the overall performance and reliability of the proposed system.
Access status: Open Access ,
Quantum inspired chaotic salp swarm optimization for dynamic optimization
(Wydawnictwa AGH, 2024) Pathak, Sanjai; Mani, Ashish; Sharma, Mayank; Chatterjee, Amlan
Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which integrates the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which details the various approaches considered while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered as the rival algorithm for DOPs.
Access status: Open Access ,
The power of intelligence emerging from swarms
(Wydawnictwa AGH, 2025) Adrdor, Rachid
Swarm intelligence (SI) is a field of study that seeks to understand and model collective behaviors observed in natural social systems, such as ant colonies, bee hives, termite mounds, flocks of birds or schools of fish. The central principle of SI is that complex intelligent behaviors can emerge from the interactions of large numbers of simple individual entities, without any centralized control or monitoring. SI researchers aim to uncover the underlying principles and mechanisms behind this SI, with the aim of applying these concepts to solve complex problems in areas such as optimization, robotics, transport, IT, etc. As the field continues to evolve, SI is expected to have an increasingly significant impact on our understanding of biological systems and our ability to design intelligent systems capable of adapting and thriving in complex environments and dynamic. This article aims to introduce the reader to the field of SI, presenting its fundamental concepts, key principles, existing applications, and prospective future developments.