Affiliation:
1. Chinese Academy of Sciences
Abstract
Abstract
The CS search algorithm is a new type of bionic optimization algorithm, which is simple and efficient, and has been successfully used in classical theoretical research and engineering applications. The algorithm uses the overall update evaluation solution to solve the continuous function optimization problem. Due to the phenomenon of mutual dimensional interference, the local refinement ability and convergence speed of the algorithm will be affected to some extent. This paper introduces the evaluation strategy of one-dimensional update and rewrites the step formula of the random walk component so that the algorithm makes full use of one-dimensional evolution information. It strengthens the local search, improves the convergence speed and the quality of the solution, and gives experimental results. The calculation results of different dimensions show that: as the dimension increases, the dimension-by-dimension strategy improves the convergence speed and solution quality. Compared with the related improved CS algorithm and other evolutionary algorithms, the improved algorithm has certain competitiveness in solving multi-dimensional function optimization problems.
Publisher
Research Square Platform LLC
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