A review of typical improvement methods of swarm intelligent optimization algorithms
Meta-heuristic swarm intelligent optimization algorithms are successful and competitive global opti-mization methods.They find globally optimal solutions by simulating natural phenomena or biological behav-iors.In this paper,the typical meta-heuristic swarm intelligent optimization algorithms and their design princi-ple are introduced.Secondly,four typical improvement methods of this kind of optimization algorithm are summarized in detail.There are population initialization,adding new strategies,iterative formula adjustment,and algorithm mixing.Finally,the future improvement and development of meta-heuristic swarm intelligent optimization algorithms are prospected.
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