A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of hu...A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of human randomized searching, and the human searching behaviors. The algorithm＇s performance is studied using a challenging set of typically complex functions with comparison of differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms, and the simulation results show that SFS is competitive to solve most parts of the benchmark problems and will become a promising candidate of search algorithms especially when the existing algorithms have some difficulties in solving certain problems.展开更多
Scheduling jobs on parallel machines to minimize the total tardiness（p//T） is proved to be NP hard.A new ant colony algorithm to deal with p//T（p//T ACO） is addressed, and the computing model of mapping p//T to th...Scheduling jobs on parallel machines to minimize the total tardiness（p//T） is proved to be NP hard.A new ant colony algorithm to deal with p//T（p//T ACO） is addressed, and the computing model of mapping p//T to the ant colony optimization environment is designed.Besides, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and proved.The theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.展开更多
基金supported by the Doctor Students Innovation Foundation of Southwest Jiaotong University.
文摘A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of human randomized searching, and the human searching behaviors. The algorithm＇s performance is studied using a challenging set of typically complex functions with comparison of differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms, and the simulation results show that SFS is competitive to solve most parts of the benchmark problems and will become a promising candidate of search algorithms especially when the existing algorithms have some difficulties in solving certain problems.
基金supported by the National Natural Science Foundation of China (60573159)
文摘Scheduling jobs on parallel machines to minimize the total tardiness（p//T） is proved to be NP hard.A new ant colony algorithm to deal with p//T（p//T ACO） is addressed, and the computing model of mapping p//T to the ant colony optimization environment is designed.Besides, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and proved.The theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.
