Modified Particle Swarm Algorithm for Decentralized Swarm Agents

Kim, D.H.; Shin, Seiichi

doi:10.1109/robio.2004.1521875

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…There are often obstacles in a terrain that challenge a swarms ability to adapt to it's surroundings. Current research is interested in not only avoiding these obstacles, but also other agents in a swarm [8]; they must maneuver in the same physical space as each other without colliding. Due to the security benefits of a decentralized system and built-in obstacle avoidance, the SHARKS protocol allows for agents to navigate in an area without colliding with each other, while only knowing the location of the target and their nearest neighboring agent.…”

Section: Obstacle Avoidancementioning

confidence: 99%

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

Cooley

Wolf

Borowczak

2018

2018 IEEE International Smart Cities Conference (ISC2)

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Unmanned Aerial Vehicles (UAVs), referenced as drones, have advanced to consumer adoption for hobby and business use. Drone applications, such as infrastructure technology, security mechanisms, and resource delivery, are just the starting point. More complex tasks are possible through the use of UAV swarms. These tasks increase the potential impacts that drones will have on smart cities, modern cities which have fully adopted technology in order to enhance daily operations as well as the welfare of it's citizens. Smart cities not only consist of static mesh networks of sensors, but can contain dynamic aspects as well including both ground and air based autonomous vehicles.Networked computational devices require paramount security to ensure the safety of a city. To accomplish such high levels of security, services rely on secure-by-design protocols, impervious to security threats. Given the large number of sensors, autonomous vehicles, and other advancements, smart cities necessitates this level of security. The SHARK protocol (Secure, Heterogeneous, Autonomous, and Rotational Knowledge for Swarms) ensures this kind of security by allowing for new applications for UAV swarm technology. Enabling drones to circle a target without a centralized control or selecting lead agents, the SHARKS protocol performs organized movement among agents without creating a central point for attackers to target. Through comparisons on the stability of the protocol in different settings, experiments demonstrate the efficiency and capacity of the SHARKS protocol.

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Section: Obstacle Avoidancementioning

confidence: 99%

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

Cooley

Wolf

Borowczak

2018

2018 IEEE International Smart Cities Conference (ISC2)

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“…Previous scholars integrated PSO with MAS [42,43]. In addition, MAPSO was mostly applied to power systems.…”

Section: Introductionmentioning

confidence: 99%

Energy Storage Coordination in Energy Internet Based on Multi-Agent Particle Swarm Optimization

Liu

Wei

et al. 2018

Applied Sciences

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With the rapid development of energy Internet (EI), energy storage (ES), which is the key technology of EI, has attracted widespread attention. EI is composed of multiple energy networks that provide energy support for each other, so it has a great demand for diverse energy storages (ESs). All of this may result in energy redundancy throughout the whole EI system. Hence, coordinating ESs among various energy networks is of great importance. First of all, we put forward the necessity and principles of energy storage coordination (ESC) in EI. Then, the ESC model is constructed with the aim of economic efficiency (EE) and energy utilization efficiency (EUE) respectively. Finally, a multi-agent particle swarm optimization (MAPSO) algorithm is proposed to solve this problem. The calculation results are compared with that of PSO, and results show that MAPSO has good convergence and computational accuracy. In addition, the simulation results prove that EE plays the most important role when coordinating various ESs in EI, and an ES configuration with the multi-objective optimization of EE and EUE is concluded at last.

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A Multi-agent System-Based Parallel Model for Particle Swarm Meta-heuristic Optimization Methods

Adbi,

Mouncif

2024

Lecture Notes in Networks and Systems

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Modified Particle Swarm Algorithm for Decentralized Swarm Agents

Cited by 4 publications

References 18 publications

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

Secure and Decentralized Swarm Behavior with Autonomous Agents for Smart Cities

Energy Storage Coordination in Energy Internet Based on Multi-Agent Particle Swarm Optimization

A Multi-agent System-Based Parallel Model for Particle Swarm Meta-heuristic Optimization Methods

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