蚁群优化算法
水准点(测量)
数学优化
选择(遗传算法)
数学
计算机科学
全局优化
人工智能
地理
大地测量学
作者
Jorge Adán Fernández-Vargas,Adrián Bonilla‐Petriciolet
标识
DOI:10.1016/j.rimni.2013.06.006
摘要
This study introduces a new algorithm for the ant colony optimization (ACO) method, which has been proposed to solve global optimization problems with continuous decision variables. This algorithm, namely ACO-FRS, involves a strategy for the selection of feasible regions during optimization search and it performs the exploration of the search space using a similar approach to that used by the ants during the search of food. Four variants of this algorithm have been tested in several benchmark problems and the results of this study have been compared with those reported in literature for other ACO-type methods for continuous spaces. Overall, the results show that the incorporation of the selection of feasible regions allows the performing of a global search to explore those regions with low level of pheromone, thus increasing the feasibility of ACO for finding the global optimal solution.
科研通智能强力驱动
Strongly Powered by AbleSci AI