Quantum Dynamical Interpretation of the Mean Strategy
Fang Wang, Peng Wang, Yuwei Jiao
TL;DR
This paper uses quantum dynamics to explain and improve the mean strategy in swarm intelligence algorithms, showing it leads to better and more diverse solutions.
Contribution
A novel quantum dynamical interpretation of the mean strategy in swarm intelligence is introduced and validated empirically.
Findings
The mean strategy increases solution diversity in optimization.
It provides accurate and stable results for finding optimal solutions.
Empirical tests confirm its efficiency and effectiveness using the CEC2013 test suite.
Abstract
The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy. The empirical results indicate that implementing the mean strategy not only enhances solution diversity but also yields accurate, efficient, stable, and effective outcomes for finding the optimal solution.
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Metaheuristic Optimization Algorithms Research