Swarm Optimization Algorithm Acceleration Framework Based on Improved Monarch Butterfly Algorithm
We propose a swarm optimization algorithm acceleration framework through the improved monarch butterfly algorithm,which aims to improve the optimization characteristics and convergence efficiency of swarm intelligence algorithms.The traditional monarch butterfly algorithm has the problems of slow convergence and easy entry into local optimality.In order to solve the above problems,we introduce a series of improvement measures into the monarch butterfly algorithm,which improve the monarch butterfly algorithm and use it as an acceleration framework to integrate with other swarm intelligence algorithm.First,chaos mapping is applied to update the starting state of the group to enhance the diversity of its members,which can effectively expand the search range and use reverse learning and random interference to replace traditional movement operations,thereby improving the overall stability and preventing the algorithm from being trapped in the local optimum.In addition,nonlinear adaptive operation factors are used to strengthen the variability in the early stage to avoid local optimality,and weaken it in the later stage to search for better results in depth,thereby improving accuracy.By combining different swarm intelligence optimization algorithms for comprehensive evaluation of 10 test functions in 30 dimensions,it is verified that the proposed algorithm framework can effectively improve the convergence speed and accuracy of other swarm intelligence optimization algorithms.
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