Imperialist competitive algorithm for AUV path planning in a variable ocean

Zheng Zeng, Karl Sammut, Andrew Lammas, Fangpo He, Youhong Tang

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)402-420
    Number of pages19
    Issue number4
    Publication statusPublished - 21 Apr 2015


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