摘要群智能优化算法是基于演化（Evolution）思想的一门计算技术，因为其具备 精度高、鲁棒性好、可并行、可分布式等特点而越来越受到人们的关注和研究， 它和人工生命（AL），特别是以进化、演化为思想的方法以及遗传算法具有特殊 的关系。截至目前，涌现出了各种各样基于群体智慧的优化算法，例如人工鱼群 算法（AFSA）、萤火虫算法（FA）、蚁群算法（ACO）、粒子群优化算法（PSO）等。 大量的实践表明，群智能优化算法是一种能有效地解决全局优化问题的技术和工 具，已经在约束优化、函数优化、机器人智能控制、电力系统、工程设计问题、 生物医学、通信领域、交通运输等多个领域得到广泛的应用。相对于低等生命体， 人类具有更高级的智能，捕鱼策略正是通过对渔夫群体捕鱼行为习惯进行模拟， 属于高等智慧生物的群智能行为，因此对捕鱼策略的探讨和研究具有一定的意义。 本文主要探讨和研究基本的 FSOA 和 PSO，并针对 FSOA 的缺点，结合 PSO 加以优 化和改进。 80373

毕业论文关键词：群智能优化算法；捕鱼策略；粒子群优化算法 

Abstract Swarm intelligence optimization algorithm is evolution (Evolution) based on the idea of a computing technology, because it has high accuracy, robustness, parallel, and other characteristics can be distributed more and more attention and research it and artificial life (AL), especially on evolution, evolution of thinking method and genetic algorithm has a special relationship。 Up to now, the emergence of a variety of optimization algorithm based on swarm intelligence, such as artificial fish school algorithm (AFSA), firefly algorithm (FA), ant colony algorithm (ACO), particle swarm optimization (PSO) and the like。 A lot of practice shows that swarm intelligence optimization algorithm is an effective solution to the problem of global optimization techniques and tools have been constrained optimization, function optimization, intelligent robot control, power systems, engineering design, biomedical, communications, transportation transportation and other fields has been widely used。 With respect to the lower life forms, humans have a more advanced intelligence, fishing policy is through the group of fisherman fishing behavior simulation, swarm intelligence behavior is higher intelligent beings, and therefore on fishing policy discussion and research have certain meaning。 This paper discusses the basic research and FSOA and PSO, and for the shortcomings FSOA, combined with the PSO to be optimized and improved。

Keywords: intelligent optimization algorithm; fishing strategy; particle swarm optimization

目录 

第一章 绪论 1

1。1 智能算法的研究背景及意义 1

1。2 群智能算法的应用 2

1。3 群智能算法的特性 3

1。3。1 智能性 3

1。3。2 隐含本质的并行性 4

1。3。3 解的近似性 4

1。4 群智能算法的优点 4

1。5 捕鱼策略优化算法的研究现状 5

1。6 论文安排