摘要
分别以遗传算法与蚁群算法对23个站位的海洋渔业资源调查路径进行规划,寻找其最优路径。求解结果表明,遗传算法和蚁群算法都能找到同样的最短路径,比实际路径缩短了8.32%的里程。蚁群算法求得的平均路径长度小于遗传算法,但所耗时间比遗传算法多一倍左右。
Searching for the optimal path is one of the most important combinatorial optimization problems. Since this problem belongs to NP-hard problems, an exact algorithm could not solve the large-scale problems in time, some metaheuristic approaches have been used to solve it in recent years. Genetic algorithm works in a way similar to the process of natural evolution, such as inheritance, mutation, selection and crossover. A basic GA starts with a randomly generated population of candidate solutions. After the evolution of several generations, the optimal solution for the problem is obtained. Ant colony optimization algorithm is to mimic the movements of ants. Ants leave a trail of pheromones when they search for food, and the pheromone density becomes higher on shorter paths than longer ones. As more ants use a particular trail, the pheromone concentration on it increases, hence attracting more ants. Consequently, all ants follow a best path. This article presented GA and ACO for solving the path planning of 23 stations for a fishing resource survey. The results indicate that both algorithm are able to find out the same shortest path, which is 8.32% shorter than the actual path. The average path length obtained by ACO is less than that by GA, but it takes nearly twice as long. It is suggested that ACO has better convergence and more aeurate calculation results, as well as GA is suitable for fastsolving and roughly estimating the problems.
出处
《海洋渔业》
CSCD
北大核心
2016年第1期83-87,共5页
Marine Fisheries
基金
农业部专项近海资源调查(2014)
关键词
路径规划
遗传算法
蚁群算法
海洋渔业
资源调查
path planning
genetic algorithm
ant colony optimization
marine fishery
resources survey
