研究目的
Investigating the therapeutic effects of a specific herbal medicine on a particular disease.
研究成果
The proposed SAPSO algorithm demonstrates improved performance over the standard PSO algorithm, with faster convergence speed and stronger global search ability. It effectively addresses the 'premature' phenomenon in PSO and is suitable for complex function global optimization problems.
研究不足
The technical and application constraints of the experiments, as well as potential areas for optimization, are not explicitly mentioned.
1：Experimental Design and Method Selection:
The methodology involves integrating the classical particle swarm algorithm with the simulated annealing particle swarm algorithm to improve UAV mission safety in complex spatial environments.
2：Sample Selection and Data Sources:
The study uses two typical test functions, Rastrigrin and Rosenbrock, to evaluate the algorithm's performance.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The algorithm's implementation steps include initializing particles, updating their positions and velocities, and comparing iteration results to find the optimal solution.
5：Data Analysis Methods:
The performance is compared based on the average optimal adaptive value and the optimal adaptive value from 50 runs of each test case.
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