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On some applications of ant colony optimization metaheuristic to plane truss optimization

机译:蚁群优化元启发式在平面桁架优化中的一些应用

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Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited to solve real-world combinatorial optimization problems. ACO algorithms, published for the first time in 1991 by M. Dorigo [Optimization, learning and natural algorithms (in Italian). Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, 1992] and his coworkers, have been applied, particularly starting from 1999 (Bonabeau et al., Swarm intelligence: from natural to artificial systems, Oxford University Press, New York, 1999; Dorigo et al., Artificial life 5(2):137–172, 1999; Dorigo and Di Caro, Ant colony optimization: a new metaheuristic, IEEE Press, Piscataway, NJ, 1999; Dorigo et al., Ant colony optimization and swarm intelligence, Springer, Berlin Heidelberg New York, 2004; Dorigo and Stutzle, Ant colony optimization, MIT Press, Cambridge, MA, 2004), to several kinds of optimization problems such as the traveling salesman problem, quadratic assignment problem, vehicle routing, sequential ordering, scheduling, graph coloring, management of communications networks, and so on. The ant colony optimization metaheuristic takes inspiration from the studies of real ant colonies’ foraging behavior. The main characteristic of such colonies is that individuals have no global knowledge of problem solving but communicate indirectly among themselves, depositing on the ground a chemical substance called pheromone, which influences probabilistically the choice of subsequent ants, which tend to follow paths where the pheromone concentration is higher. Such behavior, called stigmergy, is the basic mechanism that controls ant activity and permits them to take the shortest path connecting their nest to a food source. In this paper, it is shown how to convert natural ant behavior to algorithms able to escape from local minima and find global minimum solutions to constrained combinatorial problems. Some examples on plane trusses are also presented.
机译:蚁群优化元启发式(ACO)代表了一类新的算法,特别适合解决现实世界中的组合优化问题。 ACO算法,1991年由M. Dorigo首次发布[优化,学习和自然算法(意大利语)。博士特别是从1999年开始,就应用了论文,《米兰电子政务学院,米兰,1992年》及其同事。(Bonabeau等人,《群智能:从自然系统到人工系统》,牛津大学出版社,纽约,1999年; Dorigo等,《人工生命》 5(2):137–172,1999; Dorigo和Di Caro,蚁群优化:一种新的启发式方法,IEEE Press,Piscataway,NJ,1999; Dorigo等,蚁群优化和群体情报,斯普林格,柏林,海德堡,纽约,2004年;多里戈和斯图兹勒,蚁群优化,麻省理工学院出版社,剑桥,马萨诸塞州,2004年),针对几种优化问题,例如旅行商问题,二次分配问题,车辆路线,顺序排序,调度,图形着色,通信网络管理等。蚁群优化元启发法从对实际蚁群觅食行为的研究中得到启发。这种殖民地的主要特征是,个人不具备解决问题​​的全局知识,而是彼此之间进行间接交流,将称为费洛蒙的化学物质沉积在地面上,这种化学物质可能会影响后续蚂蚁的选择,而这些蚂蚁往往会遵循费洛蒙浓度的路径更高。这种行为被称为“ stigmergy”,是控制蚂蚁活动并允许其采取最短路径将其巢与食物来源连接的基本机制。在本文中,它显示了如何将自然蚂蚁行为转换为能够摆脱局部最小值并找到约束组合问题的全局最小值解的算法。还提供了一些有关平面桁架的示例。

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