Improved Quantum Particle Swarm Optimization Based on Population Entropy Offset Mean Weighting
The Quantum Particle Swarm Optimization has better global search ability and is considered an extremely effective improvement to the Particle Swarm Optimization.However,there is still a problem of population diversity decay during its operation.In order to further enhance the global optimization ability of Quantum Particle Swarm Optimization,an improved Quantum Particle Swarm Optimization based on population entropy offset mean weighting is proposed,which is based on Quantum Particle Swarm Optimization in weighted mean optimal position.Dynamically associating the population entropy with the weighted range center offset value effectively enhances the traversal of the algorithm's search space and avoids premature convergence of the algorithm.By applying conventional test functions,a comparative analysis is conducted with traditional Particle Swarm Optimization,Quantum Particle Swarm Optimization,and weighted Quantum Particle Swarm Optimization,demonstrating the effectiveness of the improved algorithm proposed in the paper.
QPSOWQPSOpopulation entropyoffset mean weightingtest function
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