1、Artificial intelligence optimization 人工智能优化（AI）,张培 电气11班,What is Artificial intelligence ?,The present situation and application of Artificial intelligence .,What about the future of Artificial intelligence ?,What is Artificial intelligence ?,It is a method through the program to simulate the nature
2、 evolution method to optimize, such as the simulation of biological evolution genetic algorithm(生物进化遗传算法), or like natural selection evolution algorithm(自然选进化算法) to screening to gradually achieve a maximum(or minimum) value .,The existing AI optimization algorithm,The simulated annealing algorithm(模
3、拟退火算法) Genetic algorithm(遗传算法) Tabu search(禁忌搜索) Neural network optimization algorithm(神经网络优化算法) Chaos optimization(混沌优化) The hybrid optimization strategy(混合优化策略),Evolutionary Algorithm is belong to Genetic algorithm . Multi-objective Evolutionary Algorithm(MOEA) is an algorithm using evolutionary a
4、lgorithm to solve multi-objective problem, to make all the objectives achieve optimum as possible it can .,多目标 问题,进化 算法,优化,较优解,My main research directions Multi-objective Evolutionary Algorithm(多目标进化算法),最优解,Environment and resource allocation (环境与资源配置) Electronic and electrical engineering (电子与电气工程)
5、 Communication and network optimization (通信与网络优化) Intelligent robot (智能机器人) Space flight and aviation (航空航天) Transportation (交通运输) Financial management (金融管理),Application of AI,Holland,Kirk patrick,Kennedy Eberhart,Glover,Dorigo,1975,1983,1995,1977,1991,模仿生物种群中优胜劣汰机制的遗传算法Genetic Algorithms,基于对热力学中固体
6、物质退火机制的模拟 提出了模拟退火Simulated Annealing,受鸟群觅食行为启发, 提出了粒子群优化 Particle Swarm Optimization,通过将记忆功能引入过 程，提出了禁忌搜索Tabu Search,借鉴自然界中蚂蚁群体的觅食行为，提出蚁群优化算法Ant Colony Algorithms,development history,Future,2014,We will find the more excellent algorithms to solve more optimization problem. Such as: Many objective problem Dynamic objective problem Dynamic many objective problem ,Thank you !,
