Abstract
Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.
Highlights
Unmanned Surface vehicles (USVs), known as autonomous surface crafts, have attracted worldwide attention in commercial, scientific and military fields recently
The main contributions of this work are as follows: (1) a greedy black box is established to generate the initial swarm of particles which avoids the randomness of the traditional method; (2) the strategy of greedy selection guarantees particles to move towards a higher fitness level and keeps the swarm diversity; (3) the 2-opt operation performs effectively in maintaining locally optimal fragments of relatively inferior particles and eliminate path crossovers; (4) the improved algorithm has been successfully applied to the path planning subsystem of a USV model with the aid of multi-sensors
Swarm is known thatOptimization the conventional particle swam optimization (PSO) generates an initial swarm of particles in a random way, that that this results in some infeasible solutions and restrictsinconvergence speed and
Summary
Unmanned Surface vehicles (USVs), known as autonomous surface crafts, have attracted worldwide attention in commercial, scientific and military fields recently. When a USV performs multi-objective tasks in a complex marine environment, the number of possible paths increases exponentially with the increase of target points’ number, resulting in the so-called “exponential explosion” In this circumstance, traditional algorithms, such as the exhaustive method and branch-and-bound algorithm, are unable to find the optimal solution within reasonable time cost. The main contributions of this work are as follows: (1) a greedy black box is established to generate the initial swarm of particles which avoids the randomness of the traditional method; (2) the strategy of greedy selection guarantees particles to move towards a higher fitness level and keeps the swarm diversity; (3) the 2-opt operation performs effectively in maintaining locally optimal fragments of relatively inferior particles and eliminate path crossovers; (4) the improved algorithm has been successfully applied to the path planning subsystem of a USV model with the aid of multi-sensors.
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