元启发式荧光粒子群优化算法与理论分析研究

Meta heuristic optimization method as a hot research field of intelligent science，it's wide attention by the researchers both at home and abroad Fluorescent particle swarm optimization is affected by the nature lampyridae family of insects by luminescence mating or inspired by the foraging behavior of emerging evolution method, this method is given priority to with fluorspar family of glowworm swarm algorithm, particle swarm optimization (pso) algorithm as the auxiliary pole, embedded stochastic adaptive step length, weak global attractor, strong search and search, self-learning ability evolution strategy to shorten the cost of information exchange between individuals, using integrated evolution technology, designed a new type of fluorescent particle swarm optimization algorithm, theoretically proved that the algorithm to converge to global optimal probability 1.Finally, the classic functions and an engineering example is adopted to improve the testing, algorithm performance contrast experiment, the experimental results show that the proposed algorithm can save the group scale, reduce the number of iterations, strong robustness, catching peak number,and high precision and convergence in multi-modal function optimization is very important in the field of application. This project adopted the research methods, to enhance and improve the group of the overall performance and efficiency of intelligent optimization algorithm provides a new way and method.

元启发式优化方法作为智能科学的热点研究领域，受到国内外研究者的广泛关注。本项目受自然界荧科族昆虫通过发光求偶或觅食行为的启发，基于荧科族的生物学原理，以荧科族中的萤火虫群算法为主，以粒子群算法为辅，嵌入随机自适应步长、全局优化吸引子、强搜索、弱搜索、自学习能力等演化算子来缩短个体之间信息交流的成本，利用集成演化策略，设计出一种新型元启发式萤光粒子群全局优化算法，理论上证明该算法以概率1收敛于全局最优。最后，采用经典的测试函数和工程实例进行仿真实验，与代表性元启发式优化算法ACO、PSO、GSO等算法的性能进行数值实验对比分析，期望通过实验结果表明所提出的算法能达到节约群体规模，减少迭代次数，鲁棒性强，捕峰数目全，收敛精度高，在高维空间多目标函数优化领域有着极其重要的应用。本项目所采用的研究方法，对于提高和改进其它元启发式算法的整体性能和效率提供了一新途径和方法。

元启发式算法, 尤其是受自然智能启发的群智能算法，近年来在国内外学术界掀起新的研究高潮。该算法因其能直观、行之有效地解决复杂优化问题而在几乎所有科学、工程和工业领域得到应用。然而，元启发式优化算法的许多基础理论尚未建立完善，尽快地建立完善该领域基础理论，进一步支撑成功的工程应用是该领域迫切需要解决的难题。基于此，受自然智能行为的启发，本项目提出了多种新兴元启发式优化算法，理论推导或实验仿真证明了所提出的元启发式优化算法的全局收敛性，丰富和完善了元启发式优化算法的数学理论基础。对于设计或改进其他元启发式算法的性能和效率提供了一新途径和方法，进一步丰富了元启发式优化理论与方法及拓宽其应用范围，实现有效地解决科学研究和工程应用中出现的组合优化和工程优化等难解问题，推动人工智能、计算机科学、智能科学等新兴交叉学科迅速地发展和完善，都有着非常重要的理论意义和应用价值。. 本项目已在国际学术期刊发表或录用SCI 论文41篇； JCR 二区以上：18 篇。已被SCI 引用186次；单篇论文引用次数高达40次。获授权国家发明专利3个。