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Modeling Swarm Intelligence Algorithms for CPS Swarms

ACM SIGAda Ada Letters, 2020
Swarms of cyber-physical systems (CPSs) have a high potential for innovative and successful applications. Swarm intelligence algorithms are one approach to handle the increased complexity that comes with the high number of CPSs in a swarm. In such algorithms, individual CPSs follow simple rules that lead to an emergent behavior.
M. Schranz, M. Sende
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Strategies for Parallelizing Swarm Intelligence Algorithms

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2015
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Cicirelli Franco   +5 more
openaire   +4 more sources

Overview of Algorithms for Swarm Intelligence

2011
Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI
Shu-Chuan Chu 0001   +3 more
openaire   +2 more sources

Swarm Intelligence Algorithms for Portfolio Optimization

2010
Swarm Intelligence (SI) is a relatively new technology that takes its inspiration from the behavior of social insects and flocking animals In this paper, we focus on two main SI algorithms: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) An extension of ACO algorithm and a PSO algorithm has been implemented to solve the portfolio ...
Hanhong Zhu, Yun Chen, Kesheng Wang
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A Swarm Intelligence Algorithm Inspired by Twitter

2016
For many years, evolutionary computation researchers have been trying to extract the swarm intelligence from biological systems in nature. Series of algorithms proposed by imitating animals’ behaviours have established themselves as effective means for solving optimization problems.
Zhihui Lv   +3 more
openaire   +1 more source

Developmental Swarm Intelligence

International Journal of Swarm Intelligence Research, 2014
In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective.
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SWARM INTELLIGENCE: PSO ALGORITHM

2022
A natural metaphor is a key to solve any complex optimization problem in scientific community. [1]The classical optimization problem is unable to solve the highly non linear problem because of non-adaptability. A flexible and adaptable algorithm is needed to model the real-time problem easily. More nature inspired algorithms were emerged.
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Introduction of Swarm Intelligent Algorithms

2011
It is fairly accepted fact that one of the most important human activities is decision making. It does not matter what field of activity one belongs to. Whether it is political, military, economic or technological, decisions have a far reaching influence on our lives. Optimization techniques play an important role in structural design, the very purpose
Lijuan Li, Feng Liu
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A DSL for Swarm Intelligence Algorithms.

We propose a domain-specific language to simplify the expression of Swarm Intelligence algorithms. These algorithms are typically introduced through metaphors, requiring practitioners to manually translate them into low-level implementations.This process can obscure intent and hinder reproducibility.
Martins, Kevin, Mendes, Rui
openaire   +2 more sources

An intelligent swarm clustering algorithm using swarm similarity measure

2016 16th International Symposium on Communications and Information Technologies (ISCIT), 2016
Using big data analysis technologies to cluster user data and mine the potential of stock users is of great significance to telecom operators. In this paper, we present an intelligent swarm clustering algorithm using swarm similarity measure for this purpose.
Baisong Ren   +5 more
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