Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Zhao, Jiale; Deng, Chaoshuo; Yu, Huanhuan; Fei, Hansheng; Li, Deshun

doi:10.1016/j.comcom.2023.12.040

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…According to Hu and Li [24], an optimal global position for particles is selected through a dynamic local strategy. For unconstrained elite archiving of non-dominant solutions, Li et al [25] used the so-called 'dominance tree', a specialized data structure. By comparing the sigma values of the external file to the overall particle population, Yang and Gao [26] were able to determine the global best position for each particle.…”

Section: Current Status Of Research On Multi-objective Particle Swarm...mentioning

confidence: 99%

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Guan,

Wang,

et al. 2024

Energies

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Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

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Section: Current Status Of Research On Multi-objective Particle Swarm...mentioning

confidence: 99%

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Guan,

Wang,

et al. 2024

Energies

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A method for obtaining the starting set of formation based on IPSO

Li,

Yi,

Miao

et al. 2024

AIP Advances

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The formation of intelligent platforms is a multi-objective constraint problem. It is necessary for the multi-agent to automatically generate the path from the initial position to the specified end point, and at the same time meet the constraints of space collision avoidance on the multi-target intersection path and the constraint of the agent’s motion ability, and pursue the shortest formation time of the whole formation as much as possible. In this paper, we propose an improved method for obtaining the starting set of multi-agent formation based on particle swarm optimization. First, the starting point set of the formation is defined, and then the particle swarm optimization algorithm is selected as the optimization algorithm to find the best starting point of the formation. Then the turning performance of the intelligent platform is studied, and the assembly route of each agent is designed by combining the Dubins curve. The simulation results show that the proposed method avoids collision between agents, shortens the time of agent formation assembly, and effectively ensures the reliability of the assembly route.

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