An Improved Artificial Fish Swarm Algorithm and its Application

Gao, Sili; Wen, Yujun

doi:10.1109/icis.2018.8466458

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…b. Fish Swarm Algorithm (FSA) is a novel population-based/swarm intelligent algorithm inspired by the natural schooling behavior of fish [57,58]. c. Artificial Algae Algorithm (AAA) is a populationbased optimization algorithm inspired by the behavior of microalgae cells microalgae lifestyles such as the algal tendency, reproduction, and adaptation to the surrounding environments [55,59].…”

Section: Bioinspired Algorithms and Optimizationmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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Section: Bioinspired Algorithms and Optimizationmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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“…It should be noted that most work on the AFSA algorithm focuses on its ability to optimize and for path planning within robotics [68]. However, researchers have modified AFSA to work on robotic fish in a simulated physical environment [69].…”

Section: Flocking Algorithmsmentioning

confidence: 99%

Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish

Connor,

Joordens,

Champion

2023

Bioinspir. Biomim.

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This study proposes a novel flocking algorithm for underwater robotics based on the collective behaviors of schooling fish. The algorithm, Fish-Inspired Robotic Algorithm (FIRA), integrates standard flocking behaviors such as Attraction, Alignment, and Repulsion, as well as predator avoidance, foraging, general obstacle avoidance, and wandering. Compared with traditional flocking algorithms, FIRA includes predictive elements to counteract processing delays from sensors and prioritizes the highest cluster of agents on a single side, leading to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. To address the challenges of underwater communication, FIRA is designed to work with high-latency, non-guaranteed communication methodology based on stigmergy methods found in nature, enabling practical, multi-agent, inexpensive, and tetherless communication. FIRA aims to provide a computational light control algorithm designed to work with low-cost, low-computing agents to further research using robotics to assimilate into a school of biological fish. To demonstrate the effectiveness of FIRA, it was simulated within Unity using a digital twin of a bio-inspired robotic fish, which incorporates the robot's motion and sensors in a realistic, real-time environment, with the algorithm used to direct the movements of individual agents. The performance of FIRA was compared against other collective motion algorithms to determine its effectiveness as a flocking algorithm. From the experiments, FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.

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“…Exploration of the search space for the best potential answer is based on the cycled position of search agents. S. Gao [22] presented a divided algorithm called as Improved Artificial Fish Swarm (IAFS) algorithm. It is based on searching a mathematical function, its swarming and chasing through various means, and finally its leaping behavior.…”

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

2022

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Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms.

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An Improved Artificial Fish Swarm Algorithm and its Application

Cited by 9 publications

References 7 publications

Applications and Challenges of Artificial Intelligence in Space Missions

Applications and Challenges of Artificial Intelligence in Space Missions

Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

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