计算机科学
稳健性(进化)
运动规划
帝国主义竞争算法
数学优化
变量(数学)
粒子群优化
算法
路径(计算)
人工智能
机器人
数学
多群优化
数学分析
生物化学
化学
基因
程序设计语言
作者
Zheng Zeng,Karl Sammut,Andrew Lammas,Fangpo He,Youhong Tang
标识
DOI:10.1080/08839514.2015.1004614
摘要
An imperialist competitive algorithm (ICA) is introduced for solving the optimal path planning problem for autonomous underwater vehicles (AUVs) operating in turbulent, cluttered, and uncertain environments. ICA is a new sociopolitically inspired global search metaheuristic based on a form of competition between “imperialist” forces and opposing colonies. In this study, ICA is applied to optimize the coordinates of a set of control points for generating a curved spline path. The ICA-based path planner is tested to find an optimal trajectory for an AUV navigating through a variable ocean environment in the presence of an irregularly shaped underwater terrain. The genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO) are described and evaluated with the ICA for the path optimization problem. Simulation results show that the proposed ICA approach is able to obtain a more optimized trajectory than the GA- or QPSO-based methods. Monte Carlo simulations demonstrate the robustness and superiority of the proposed ICA scheme compared with the GA and QPSO schemes.
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