Chapter 8 Self-Organized Load Balancing through Swarm Intelligence

2011 ◽  
pp. 195-224 ◽  
Author(s):  
Vesna Šešum-Čavić ◽  
Eva Kühn
Keyword(s):  
Load Balancing ◽  
Swarm Intelligence ◽  
Self Organized

scholarly journals Generalized swarm intelligence algorithms with domain-specific heuristics

2021 ◽  
Vol 10 (1) ◽  
pp. 157
Author(s):  
P. Matrenin ◽  
V. Myasnichenko ◽  
N. Sdobnyakov ◽  
D. Sokolov ◽  
S. Fidanova ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Minimum Distance ◽  
Heuristic Algorithms ◽  
Population Based ◽  
Scheduling Problem ◽  
Domain Specific ◽  
Hybrid Approaches ◽  
Self Organized ◽  
Swarm Algorithms ◽  
Tasks Scheduling

<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

Autonomic Networking

2008 ◽  
pp. 72-78
Author(s):  
Pantelis N. Karamolegkos ◽  
Charalampos Patrikakis ◽  
Emmanuel Protonotarios
Keyword(s):  
Load Balancing ◽  
Swarm Intelligence ◽  
Resource Discovery ◽  
Distributed Hash Tables ◽  
Hash Tables ◽  
Autonomic Networking ◽  
Agent Based ◽  
Computer Communication ◽  
Novel Algorithms ◽  
Innovative Techniques

The term “autonomic networking” refers to a recently emerged communications paradigm, that uses distributed techniques (swarm intelligence based methods) and distributed hash tables to implement traditional functionalities of networking, including among others routing, service and resource discovery, addressing, load balancing etc. The shift of interest towards the autonomic networking approach has been mainly motivated by the pervasion of novel algorithms into relevant fields of computer communication science. These innovative techniques have been greatly alleviated by their fusion with already established solutions and applications (i.e. agent-based systems, network middleware development etc) so as to form a new ever-evolving landscape in networking.

scholarly journals Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 142
Author(s):  
Piotr Jedrzejowicz ◽  
Izabela Wierzbowska
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Special Class ◽  
Computational Experiment ◽  
Optimization Problems ◽  
Population Based ◽  
Apache Spark ◽  
Self Organized ◽  
Intelligence Approach

One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach in which a swarm of agents tries to improve solutions from the population of solutions. The process is carried out in parallel threads. The proposed algorithm—based on the mushroom-picking metaphor—was implemented using Scala in an Apache Spark environment. An extended computational experiment shows how introducing a combination of simple optimization agents and increasing the number of threads may improve the results obtained by the model in the case of TSP and JSSP problems.

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