利用海鸥理论的路径优化算法分析

Analysis of path optimization algorithm using seagull theory

针对GIS空间分析需要经常解决的路径优化问题,本文研究了一种新型的群体智能空间路径优化算法,即海鸥优化算法(SOA)。通过重新定义海鸥位置的表示方式和更新策略,将海鸥优化算法从连续域转换到离散域,建立离散海鸥优化算法(DSOA),同时引入随机异变因子,使海鸥有能力跳出局部最优值。为了验证DSOA的可靠性,通过定义适应度函数和可行解空间,实现利用离散海鸥优化算法求解经典的旅行商最短路径问题。试验结果表明,DSOA在解决最优路径问题上具有良好的稳健性,在空间分析方面具有较强应用潜力。

Aiming at the path optimization problem that needs to be solved frequently in GIS spatial analysis,this paper studies a new type of swarm intelligent space path optimization algorithm,called seagull optimization algorithm(SOA).By redefining the representation and update strategy of seagull position,the seagull optimization algorithm is converted from continuous domain to discrete domain,and then the discrete seagull optimization algorithm is established(DSOA).At the same time,in order to make the seagull to jump out of the local optimal value,a random variable factor is introduced.In order to verify the reliability of DSOA,by defining the fitness function and feasible solution space,the discrete seagull optimization algorithm is used to solve the traveling salesman problem(TSP).The results experimental results show that DSOA has good robustness in solving optimal path problems and has strong application potential in spatial analysis.