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The Application of an Improved PSO Based on the Quantum Genetic Algorithm in the Submersible Path-planning

Author: LiuYangLei
Tutor: YuFei
School: Harbin Engineering University
Course: System theory
Keywords: Particle swarm optimization Quantum genetic algorithm Submarine Path-planning
Type: Master's thesis
Year: 2011
Downloads: 61
Quote: 0
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With ocean resources drawing more and more attention, people are having higher expect ion of the resource exploration and development completed by undersea vehicles, thus higher requirements of intelligence and autonomous navigation are needed. Path-planning technique is one of its intelligent navigation control essential technologies, which has direct relations with undersea vehicles intelligence level.The particle swarm optimization algorithm (hereinafter referred to as PSO) is a new type of intelligent swarm algorithm. As a kind of parallel stochastic optimization algorithm, PSO doesn’t rely on derivative information, but rather through simple iteration between individual particles and information sharing among swarms to realize complex search. Also it can solve nonlinear, non-differentiable and Multi-peak morbid function optimization problem, and has been widely applied to in various optimization fields. Underwater path planning is an important application. But, the PSO is not satisfied in this respect, and the mainly problem is that it is difficult to determine the parameters in high-dimension problems. Thus, on the basis of the quantum genetic algorithm, we improve the PSO. Firstly a non-linear decreasing inertia weight is used in standard PSO instead of linear decreasing inertia weight. Then, based on the quantum genetic algorithm, the quantum gate is introduced into PSO, to real-tine adjusts corresponding parameters, which can fully combine the quantum genetic algorithm and the particle swarm optimization algorithm advantage. Comparing the improved algorithm and standard PSO algorithm, we get the following result:the improved is superior to the standard PSO in optimization ability and the convergence rate, and it can find the optimal path faster.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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