【摘 要】
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An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying s
【机 构】
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School of Mechanical and Vehicular Engineering
论文部分内容阅读
An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction (SIA) method or combinatorial auction method to multi-robot task allocation.The genetic algorithm based combinatorial auction (GACA) method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem (WDP) of combinatorial auction.The simulation experiments are conducted in OpenSim, a multi-robot simulator.The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction (PGACA) method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.
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