Recent Developments in the Theory and Applicability of Swarm Search

Altshuler, Yaniv

doi:10.3390/e25050710

Cited by 2 publications

(1 citation statement)

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“…Researchers model and create algorithms based on this intelligence. These algorithms are classified as Nature-Inspired or Swarm Intelligence Optimization algorithms or metaheuristics and have been applied to solve a diverse range of problems (Fakhermand & Derakhshani, 2023;Tzanetos & Dounias, 2020;Engelbrecht, 2007;Alizadehsani, et al, 2023;Altshuler, 2023;Shahzad, et al, 2023;Kumar, Chatterjee, Payal, & Rathore, 2022;Cruz, Maia, & de Castro, 2021).…”

Section: Introductionmentioning

confidence: 99%

Foraging Bee Optimization Algorithm

Fasina,

Sawyerr,

Alkassim

2023

IJIEM

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Honey bee colonies depend on pollen and nectar from flowers for their feed. The act of searching for this flowers by the bees is called foraging. The foraging behaviour of bees depends on the profitability of nectar and pollen sources as well as the needs of the colony. This behaviour is modeled into an algorithm called Foraging Bee Optimization Algorithm (FBA). After initialization, the algorithm loops through three phases based on bees’ nature foraging behaviour called the 3W: Waggle, Work, and Withdraw. A large number of flowers are initialized randomly in the problem space. During the waggle phase, bees are recruited to patch with profitable nectar sources. In the work phase, new flowers are discovered and memorized by bees. In the withdraw phase bees eliminate unprofitable flowers and recalibrate for recruitment. The proposed FBA is tested on three unimodal and twelve multimodal benchmark. The result is compared with two state-of-the-art natured-inspired optimization algorithm.

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Section: Introductionmentioning

confidence: 99%

Foraging Bee Optimization Algorithm

Fasina,

Sawyerr,

Alkassim

2023

IJIEM

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Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems

Yazıcı,

Akdemir Ömür,

Askun Celik

2024

Sosyal Mucit Academic Review

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This paper examines the potential implications of the principles of swarm intelligence and collective behavior in nature for unmanned systems and autonomous organizational structures. Swarm intelligence is inspired by natural systems in which individual units interact according to simple rules to form a complex and organized whole. These principles can be observed in a wide range of situations, from the synchronized flight of flocks of birds to the harmonized swimming behavior of schools of fish. The study emphasizes that swarm intelligence principles have the potential to create more flexible, resilient and efficient systems with decentralized control mechanisms and autonomous decision-making processes. Furthermore, it is suggested that these approaches can find applications in many fields, from military operations to agricultural and environmental monitoring, from disaster response to urban planning. The study provides a detailed analysis of swarm behavior in nature and discusses how these behaviors can be emulated and optimized in unmanned systems. In this context, the potential impacts of swarm intelligence and collective behavior principles on unmanned systems are evaluated in terms of increasing their adaptability, optimizing energy efficiency and maximizing mission success. It is also argued that these principles can contribute to making unmanned systems more resilient to contingencies and changing environmental conditions. This theoretical study suggests that swarm intelligence principles can provide innovative solutions for future unmanned systems and autonomous organizational structures.

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Recent Developments in the Theory and Applicability of Swarm Search

Abstract: Swarm intelligence (SI) is a collective behaviour exhibited by groups of simple agents, such as ants, bees, and birds, which can achieve complex tasks that would be difficult or impossible for a single individual [...]

Cited by 2 publications

References 124 publications

Foraging Bee Optimization Algorithm

Foraging Bee Optimization Algorithm

Applications and Future Perspectives of Swarm Intelligence in Unmanned and Autonomous Systems

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