关键词: 磷虾群算法, 近邻套索算子, 函数优化, 开采能力

Abstract: Aiming at the problem that basic Krill Herd（KH） algorithm has poor local search ability and insufficient exploitation capacity when solving complex function optimization problems, an NLKH algorithm based on nearest neighbor lasso operator is proposed in this paper. In this algorithm, a new nearest neighbor lasso operator is added to the basic krill herd algorithm, which makes it more effective to deal with the optimization problem of complex functions. The nearest neighbor lasso operator selects the target krill pair by comparing the Euclidean distance between krill individuals. Then the local search ability of the krill individual is improved by accelerating the operation of high quality krill individuals and eliminating poorly performing krill individuals. By comparing PSO algorithm, KH algorithm, KHLD algorithm and NLKH algorithm on 10 test functions, the results show that NLKH algorithm has stronger global search ability, higher accuracy, faster convergence speed and better stability. And the NLKH algorithm has stronger local search ability than the KH algorithm and KHLD algorithm, and exploitation ability is also stronger.

Key words: krill herd algorithm, nearest neighbor lasso operator, function optimization, exploitation ability