摘 要
动态优化问题广泛存在于工程调度、资源分配和路径规划等领域，其复杂性和不确定性对传统优化方法提出了严峻挑战。为提升遗传算法在动态环境下的搜索能力和适应性，本文提出一种基于改进遗传算法的动态优化求解方法。该方法引入自适应变异机制与双种群协同策略，结合局部搜索与全局探索，以增强算法在变化环境中的响应速度与收敛精度。通过构建具有时变特性的测试函数集，验证了算法在不同动态场景下的稳定性与鲁棒性。实验结果表明，相较于传统遗传算法，所提方法在求解质量和收敛速度方面均有显著提升，尤其在高维多峰函数优化中表现出更强的适应能力。本研究不仅拓展了遗传算法的应用范围，也为动态优化问题提供了新的解决思路和技术路径，具有一定的理论价值与实际应用前景。
关键词：动态优化；遗传算法改进；双种群协同策略；自适应变异机制；时变测试函数集
ABSTRACT
Dynamic optimization problems are widely present in fields such as engineering scheduling, resource allocation, and path planning, where their complexity and uncertainty pose significant challenges to traditional optimization methods. To enhance the search capability and adaptability of genetic algorithms in dynamic environments, this paper proposes a dynamic optimization solution based on an improved genetic algorithm. The method introduces an adaptive mutation mechanism and a dual-population cooperative strategy, integrating both local search and global exploration to improve the algorithm's response speed and convergence accuracy in changing environments. A set of test functions with time-varying characteristics is constructed to validate the stability and robustness of the proposed approach across different dynamic scenarios. Experimental results demonstrate that compared to traditional genetic algorithms, the proposed method achieves significant improvements in solution quality and convergence speed, particularly exhibiting stronger adaptability in high-dimensional multimodal function optimization. This study not only expands the application scope of genetic algorithms but also provides new insights and technical pathways for addressing dynamic optimization problems, offering both theoretical value and practical application potential.
Keywords: Dynamic Optimization; Genetic Algorithm Improvement; Dual Population Cooperative Strategy; Adaptive Mutation Mechanism; Time-Varying Test Function Set
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 研究背景和意义 1
1.2 研究现状 1
1.3 本文研究方法 1
第二章 遗传算法在动态环境中的适应机制 3
2.1 动态优化问题的基本特征与挑战 3
2.2 遗传算法对动态变化的响应策略 3
2.3 基于种群多样性的适应性增强方法 3
2.4 动态环境下遗传算子的设计原则 4
第三章 多样性保持策略的优化设计 5
3.1 多样性保持对动态优化的重要性 5
3.2 基于重启机制的多样性恢复方法 5
3.3 自适应变异率调整策略研究 6
3.4 基于记忆机制的历史信息利用方式 6
第四章 动态环境下算法性能评估与比较 7
4.1 动态测试函数的设计与分类 7
4.2 性能评价指标体系构建 7
4.3 不同算法在动态问题中的对比分析 7
4.4 参数敏感性与稳定性研究 8
结论 9
参考文献 10
致 谢 12
