Survey Paper on Swarm Intelligence

Keerthi, S. Sathiya; Ashwini, K; Vijaykumar, M

doi:10.5120/20145-2273

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…BA suffers premature convergence which lead to stuck in local optimum, and complexity time increase according to the dimensional search area. [ 246 , 247 , 247 ].…”

Section: Evaluation Of Bat Algorithmmentioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

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Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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“…BA suffers premature convergence which lead to stuck in local optimum, and complexity time increase according to the dimensional search area. [ 246 , 247 , 247 ].…”

Section: Evaluation Of Bat Algorithmmentioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…By utilizing multiple search candidates and meanwhile balancing between exploration and exploitation, they provided higher possibilities to approach the global optima. As a sub‐class of population‐based algorithms, swarm‐based algorithms 38 utilized multiple search candidates for the purpose of exploration. These search candidates then iteratively evolve, and eventually the healthier individuals will survive, making the exploitation become possible.…”

Section: Related Workmentioning

confidence: 99%

PECCO: A profit and cost‐oriented computation offloading scheme in edge‐cloud environment with improved Moth‐flame optimization

Dai

Wang

et al. 2022

Concurrency and Computation

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With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource-rich cloud centres have been utilised to tackle these challenges. To relieve the burden on cloud centres, edge-cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and Quality of Service (QoS). Several optimisation models of edge-cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and costoriented computation offloading optimisation model PECCO proposed in this paper.Considering that the model is hard in nature and the optimisation objective is not differentiable, we propose an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of the original Moth-flame Optimiser and integrate it under the edge-cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimising the proposed task offloading model under the edge-cloud environment.

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“…The BCO algorithm was also devised to find optimal solutions to distributed control system problems such as scheduling, clustering and engineering designs taking inspiration from the collective intelligence of bee’s behavior in food search [ 92 ]. The difference towards other algorithms such as PSO and ACO is that the BCO algorithm relies on the different roles that bees have in a colony.…”

Section: System Componentsmentioning

confidence: 99%

Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Asaamoning

Mendes

Rosário

et al. 2021

Sensors

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The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

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Survey Paper on Swarm Intelligence

Cited by 11 publications

References 14 publications

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

PECCO: A profit and cost‐oriented computation offloading scheme in edge‐cloud environment with improved Moth‐flame optimization

Drone Swarms as Networked Control Systems by Integration of Networking and Computing

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