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

    6 Citations (Scopus)

    Abstract

    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
    JournalAPPLIED ARTIFICIAL INTELLIGENCE
    Volume29
    Issue number4
    DOIs
    Publication statusPublished - 21 Apr 2015

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