摘 要
遗传算法作为一种全局优化搜索方法，在众多领域得到广泛应用，适应度函数作为遗传算法的核心组件，直接影响算法性能和求解效果。为此，本文聚焦于遗传算法的适应度函数设计与优化策略展开研究。在分析传统适应度函数局限性的基础上，提出一种基于多目标动态调整机制的适应度函数模型，该模型通过引入自适应权重因子和非线性缩放技术，有效解决了单一静态适应度函数难以兼顾全局探索与局部开发的问题。同时，针对特定优化问题，构建了融合领域知识的复合型适应度函数框架，增强了算法对复杂环境的适应能力。实验结果表明，所提出的适应度函数设计方法能够显著提高遗传算法的收敛速度和解的质量，在多个标准测试函数及实际应用案例中均展现出优越性能。此外，本研究还探讨了不同参数设置对适应度函数的影响规律，为后续研究提供了理论依据和技术参考，主要贡献在于创新性地将多目标优化思想融入适应度函数设计，并实现了理论与实践的有效结合，推动了遗传算法在复杂优化问题求解方面的进步。
关键词：遗传算法 适应度函数设计 多目标动态调整
Abstract
Genetic algorithms, as a global optimization search method, have been widely applied in various fields. The fitness function, serving as a core component of genetic algorithms, directly influences algorithm performance and solution quality. This study focuses on the design and optimization strategies of fitness functions for genetic algorithms. By analyzing the limitations of traditional fitness functions, a multi-ob jective dynamically adjusted fitness function model is proposed. This model incorporates adaptive weighting factors and nonlinear scaling techniques, effectively addressing the challenge of balancing global exploration and local exploitation that single static fitness functions struggle with. Additionally, for specific optimization problems, a composite fitness function fr amework integrated with domain knowledge is constructed, enhancing the algorithm's adaptability to complex environments. Experimental results demonstrate that the proposed fitness function design approach significantly improves the convergence speed and solution quality of genetic algorithms, showing superior performance across multiple standard test functions and real-world application cases. Furthermore, this research explores the impact patterns of different parameter settings on the fitness function, providing theoretical foundations and technical references for future studies. The primary contribution lies in innovatively integrating multi-ob jective optimization concepts into fitness function design, achieving effective combination of theory and practice, and advancing the application of genetic algorithms in solving complex optimization problems.
Keyword:Genetic Algorithm Fitness Function Design Multi-ob jective Dynamic Adjustment
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2适应度函数的基本理论 2
2.1适应度函数的定义与作用 2
2.2适应度函数的设计原则 3
2.3适应度函数的评价指标 3
3适应度函数设计的影响因素 4
3.1问题特征对设计的影响 4
3.2搜索空间的特性分析 5
3.3多目标优化中的权衡 5
4适应度函数的优化策略 6
4.1参数调整与优化 6
4.2动态适应度函数设计 6
4.3混合优化策略应用 7
结论 8
参考文献 9
致谢 10
