首页|Study Data from Qinghai Normal University Update Knowledge of Robotics (A Multi-strategy Genetic Algorithm for Solving Multipoint Dynamic Aggregation Problems With Priority Relationships of Tasks)
Study Data from Qinghai Normal University Update Knowledge of Robotics (A Multi-strategy Genetic Algorithm for Solving Multipoint Dynamic Aggregation Problems With Priority Relationships of Tasks)
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Amer Inst Mathematical Sciences-Aims
Current study results on Robotics have been published. According to news reporting out of Xining, People's Republic of China, by NewsRx editors, research stated, "The multi-point dynamic aggregation problem (MPDAP) that arises in practical applications is characterized by a group of robots that have to cooperate in executing a set of tasks distributed over multiple locations, in which the demand for each task grows over time. To minimize the completion time of all tasks, one needs to schedule the robots and plan the routes." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Qinghai Normal University, "Hence, the problem is essentially a combinatorial optimization problem. The manuscript presented a new MPDAP in which the priority of the task was considered that is to say, some tasks must be first completed before others begin to be executed. When the tasks were located at different priority levels, some additional constraints were added to express the priorities of tasks. Since route selection of robots depends on the priorities of tasks, these additional constraints caused the presented MPDAP to be more complex than ever. To efficiently solve this problem, an improved optimization algorithm, called the multi-strategy genetic algorithm (MSGA), was developed. First of all, a two-stage hybrid matrix coding scheme was proposed based on the priorities of tasks, then to generate more route combinations, a hybrid crossover operator based on 0-1 matrix operations was proposed. Furthermore, to improve the feasibility of individuals, a repair schedule was designed based on constraints. Meanwhile, a q-tournament selection operator was adopted so that better individuals can be kept into the next generation."
XiningPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesGenetic AlgorithmsGeneticsMachine LearningNano-robotRoboticsQinghai Normal University
NETL
NSTL
Amer Inst Mathematical Sciences-Aims
