Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints
Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints
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不确定时间约束下救援任务分配优化的粒子群优化算法
DOI：
10.1007/s40747-020-00252-2
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
巩敦卫
中科院分区：
文献类型：
--
作者：
耿娜;Zhiting Chen;Quang A. Nguyen;巩敦卫
本文聚焦于机器人救援任务分配问题，其中采用多个机器人和一种全局最优算法来规划救援任务分配。相应地，一种改进的粒子群优化（PSO）算法，被称为……
This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.
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