As the Internet has become more popular and rural e-commerce has developed rapidly, rural residents have begun to purchase household goods through e-commerce platforms. Therefore, this study applied a biomimetic algor...
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Inspired by killer whale hunting strategies, this study presents a biomimetic algorithm for controlled subgroup fission in swarms. The swarm agents adopt the classic social force model with some practical modification...
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Inspired by killer whale hunting strategies, this study presents a biomimetic algorithm for controlled subgroup fission in swarms. The swarm agents adopt the classic social force model with some practical modifications. The proposed algorithm consists of three phases: cluster selection phase via a constrained K-means algorithm, driven phase with strategic agent movement, including center pushing, coordinated oscillation, and flank pushing by specialized driven agents, and judgment phase confirming subgroup separation using the Kruskal algorithm. Simulation results confirm the algorithm's high success rate and efficiency in subgroup division, demonstrating its potential for advancing swarm-based technologies.
This study explores the efficiency of the Particle Swarm Optimization (PSO) algorithm in optimizing fish swarm transportation tasks. Our findings demonstrate that PSO effectively reduces the number of particles and it...
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ISBN:
(纸本)9798350388350;9798350388343
This study explores the efficiency of the Particle Swarm Optimization (PSO) algorithm in optimizing fish swarm transportation tasks. Our findings demonstrate that PSO effectively reduces the number of particles and iterations, rapidly converging to a solution that approximates the real distribution ratio. The particles dynamically update their speed and direction, maintaining a record of all past trajectories to avoid redundancy. As the iterations progress, the search range narrows, facilitating each particle's access to comprehensive group data, which directs them purposefully towards the optimal distribution, optimizing resource use and operational efficiency. Crucially, parameter adjustments, particularly the influence factor phi and movement step size, are essential for fine-tuning the particles' speed and optimizing convergence, requiring precise calibration to avoid suboptimal local solutions.
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