Traveling salesman problem(TSP) is one of the typical NP-hard problems, and it has been used in many engineering applications. However, the previous swarm intelligence(SI) based algorithms for TSP cannot coordinate wi...Traveling salesman problem(TSP) is one of the typical NP-hard problems, and it has been used in many engineering applications. However, the previous swarm intelligence(SI) based algorithms for TSP cannot coordinate with the exploration and exploitation abilities and are easily trapped into local optimum. In order to deal with this situation, a new hybrid optimization algorithm based on wolf pack search and local search(WPS-LS)is proposed for TSP. The new method firstly simulates the predatory process of wolf pack from the broad field to a specific place so that it allows for a search through all possible solution spaces and prevents wolf individuals from getting trapped into local optimum. Then, local search operation is used in the algorithm to improve the speed of solving and the accuracy of solution. The test of benchmarks selected from TSPLIB shows that the results obtained by this algorithm are better and closer to the theoretical optimal values with better robustness than those obtained by other methods.展开更多
To help the people choose a proper medical treatment organizer, this paper proposes an opposition raiding wolf pack optimization algorithm using random search strategy(ORRSS-WPOA) for an adaptive shrinking region. Fir...To help the people choose a proper medical treatment organizer, this paper proposes an opposition raiding wolf pack optimization algorithm using random search strategy(ORRSS-WPOA) for an adaptive shrinking region. Firstly, via the oppositional raiding method(ORM), each wolf has bigger probability of approaching the leader wolf, which makes the exploration of the wolf pack enhanced as a whole. In another word, the wolf pack is not easy to fall into local optimum. Moreover, random searching strategy(RSS) for an adaptive shrinking region is adopted to strengthen exploitation, which enables any wolf to be more likely to find the optimum in some a given region, so macroscopically the wolf pack is easier to find the global optimal in the given range. Finally, a fitness function was designed to judge the appropriateness between a certain patient and a hospital. The performance of the ORRSS-WPOA was comprehensively evaluated by comparing it with several other competitive algorithms on ten classical benchmark functions and the simulated fitness function aimed to solve the problem mentioned above. Under the same condition, our experimental results indicated the excellent performance of ORRSS-WPOA in terms of solution quality and computational efficiency.展开更多
基金the National Natural Science Foundation of China(No.61502198)the Science&Technology Development Project of Jilin Province(Nos.20180101334JC and 20190302117GX)the"3th-Five Year" Science and Technology Research Project of Education Department of Jilin Province(No.JJKH20170574KJ)
文摘Traveling salesman problem(TSP) is one of the typical NP-hard problems, and it has been used in many engineering applications. However, the previous swarm intelligence(SI) based algorithms for TSP cannot coordinate with the exploration and exploitation abilities and are easily trapped into local optimum. In order to deal with this situation, a new hybrid optimization algorithm based on wolf pack search and local search(WPS-LS)is proposed for TSP. The new method firstly simulates the predatory process of wolf pack from the broad field to a specific place so that it allows for a search through all possible solution spaces and prevents wolf individuals from getting trapped into local optimum. Then, local search operation is used in the algorithm to improve the speed of solving and the accuracy of solution. The test of benchmarks selected from TSPLIB shows that the results obtained by this algorithm are better and closer to the theoretical optimal values with better robustness than those obtained by other methods.
基金supported by the National Key Research and Development Program of China (2016YFB0700502,2016YFB1001404)the National Natural Science Foundation of China (51761135121)
文摘To help the people choose a proper medical treatment organizer, this paper proposes an opposition raiding wolf pack optimization algorithm using random search strategy(ORRSS-WPOA) for an adaptive shrinking region. Firstly, via the oppositional raiding method(ORM), each wolf has bigger probability of approaching the leader wolf, which makes the exploration of the wolf pack enhanced as a whole. In another word, the wolf pack is not easy to fall into local optimum. Moreover, random searching strategy(RSS) for an adaptive shrinking region is adopted to strengthen exploitation, which enables any wolf to be more likely to find the optimum in some a given region, so macroscopically the wolf pack is easier to find the global optimal in the given range. Finally, a fitness function was designed to judge the appropriateness between a certain patient and a hospital. The performance of the ORRSS-WPOA was comprehensively evaluated by comparing it with several other competitive algorithms on ten classical benchmark functions and the simulated fitness function aimed to solve the problem mentioned above. Under the same condition, our experimental results indicated the excellent performance of ORRSS-WPOA in terms of solution quality and computational efficiency.
