现代信息科技2026，Vol.10Issue(11)：117-125,9.DOI:10.19850/j.cnki.2096-4706.2026.11.021
多策略融合改进的梦境优化算法
Multi-Strategy Fusion Enhanced Dream Optimization Algorithm
万赛 1朱佳鑫 1文嘉俊 1黄涛 1张爱佳1
作者信息
- 1. 辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105
- 折叠
摘要
Abstract
To address the limitations of the dream optimization algorithm(DOA)in initial population uniformity,exploration adaptability,and exploitation-stage global escape capability,this paper proposes a Multi-strategy fusion Improved Dream Optimization Algorithm(MIDOA).First,piecewise linear chaotic mapping combined with Quasi-Opposition-Based Learning is employed for population initialization to generate high-quality uniformly distributed individuals.Second,an adaptive search mechanism based on dream state classification is developed by monitoring stagnation degree to enable adaptive search intensity adjustment.Finally,an adaptive Lévy flight strategy is incorporated in the exploitation phase through a multi-strategy jumping mechanism to enhance escape ability from local optima.Experimental results on CEC2017 benchmarks show that MIDOA achieves optimal optimization accuracy and stability simultaneously on 6 functions.Compared with WOA,GWO,SCA,and SSA,MIDOA obtains optimal results on over 70％of test functions,with convergence accuracy improved by 1～2 orders of magnitude.The Wilcoxon rank-sum test validates statistical significance.The multi-strategy fusion approach effectively enhances convergence speed,optimization precision,and global search capability.关键词
梦境优化算法/分段线性混沌映射/准反向学习/梦境状态分类/自适应Lévy飞行Key words
Dream Optimization Algorithm/Piecewise Linear Chaotic Map/Quasi-Opposition-Based Learning/dream state classification/adaptive Lévy flight分类
信息技术与安全科学引用本文复制引用
万赛,朱佳鑫,文嘉俊,黄涛,张爱佳..多策略融合改进的梦境优化算法[J].现代信息科技,2026,10(11):117-125,9.
