Perceptualization of particle swarm optimization

Kirschenbaum, Marc; Palmer, William D.

doi:10.1109/shbi.2015.7321681

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…An apparatus that stores as well as discharges lot of energy at the same time called SMES. It saves energy by storing it inside a magnetic flux which is established on flowing DC current through the coils of superconductor as well as cooling it with a cryogenic [35,36]. For a few years, the SMES system has been employed to get better quality of power and to supply fine voltage management when voltage fluctuation occurs as shown in Figure 3 [41].…”

Section: Superconducting Magnetic Energy Storage (Smes)mentioning

confidence: 99%

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A Review on Frequency Stability Enhancement and Effective Energy Storage through Various Optimization Techniques

Mishra

Ralhan

Mohiddin

2022

Mobile Information Systems

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In recent years, the applicability of these DG units to electrical Microgrids (MGs) has grown rapidly, enabling them to contribute a large percentage of the installed generating capacity. However, the fluctuating and intermittent nature of renewable generation can adversely affect electric grid stability and operations. Conventionally, to overcome these problems, batteries are employed. Nevertheless, the quick charging and discharging cycle reduces the battery life span, resulting in an economic burden and environmental damage. To resolve these problems, short-term Distributed Energy Storage (DES) systems based on advanced technologies, such as Superconducting Magnetic Energy Storage (SMES) and Supercapacitor Energy Storage (SCES), are emerging as potential alternatives. A supplementary regulator which includes storing energy as well as a flexible AC transmission system is designed to boost the minute signal reliability. The precise optimization of multiple energy device characteristics is needed for efficient performance. The artificial intelligence methods and Particle Swarm Optimization (PSO) are used to obtain the optimum parameters in the micro-hydro system. This research examines the many Energy Storage Systems (ESSs) in power systems, particularly microgrids, and demonstrates their critical role in improving the quality of electrical systems. As a result, the ESSs were divided into several technologies based on the energy storage form and the most important technological features. In this review paper, the most common classifications are introduced/presented, summarized, and compared according to their characteristics.

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Section: Superconducting Magnetic Energy Storage (Smes)mentioning

confidence: 99%

“…Both the swarm and the cuckoo birds are present when it deposits its egg. As a result, the bird swarms either discard its egg, else abandon its nest and thereafter tossing out the new one [35,51]. Parasites of additional organism's cuckoo eggs hatch when the cuckoo hatches.…”

Section: Cuckoo Search Algorithm (Csa)mentioning

confidence: 99%

A Review on Frequency Stability Enhancement and Effective Energy Storage through Various Optimization Techniques

Mishra

Ralhan

Mohiddin

2022

Mobile Information Systems

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“…The first category of environments harnesses the collective intelligence of multiple participants to solve difficult problems. Many of these environments are aimed at solving computationally intensive problems (Barrington et al 2011;Cooper et al 2010;Eberhart et al 2015;Kirschenbaum and Palmer 2015;Lin et al 2014;Jensen et al 2020). The UNUM platform (Rosenberg et al 2016;Rosenberg 2015), also referred to as Swarm AI®, lets participants collaboratively explore a decision space.…”

Section: Related Workmentioning

confidence: 99%

HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

et al. 2021

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Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—‘Humans Go Swarming’—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence.

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“…A number of multi-user virtual environments have been developed for studies with human groups, in two main categories. The first category of environments use collective human gameplay or other interactions as tools for solving computationally intensive problems [2,6,9,18,23,44], rather than studying underlying cognitive or behavioral mechanisms. The second category of virtual environments are those developed primarily to study the mechanisms of collective human behavior.…”

Section: Related Workmentioning

confidence: 99%

HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence

Coucke

Heinrich

Cleeremans

et al. 2020

Lecture Notes in Computer Science

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The research topic of human-human swam intelligence includes many mechanisms that need to be studied in controlled experiment conditions with multiple human subjects. Virtual environments are a useful tool to isolate specific human interactions for study, but current platforms support only a small scope of possible research areas. In this paper, we present HuGoS-'Humans Go Swarming'-a multiuser virtual environment in Unity, as a comprehensive tool for experimentation in human-human swarm intelligence. We identify possible experiment classes for studying human collective behavior, and equip our virtual environment with sufficient features to support each of these experiment classes. We then demonstrate the functionality of the virtual environment in simple examples for three of the experiment classes: human collective decision making, human social learning strategies, and agent-level human interaction with artificial swarms, including robot swarms.

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Perceptualization of particle swarm optimization

Cited by 8 publications

References 1 publication

A Review on Frequency Stability Enhancement and Effective Energy Storage through Various Optimization Techniques

A Review on Frequency Stability Enhancement and Effective Energy Storage through Various Optimization Techniques

HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective

HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence

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