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作 者:张其文[1] 杨勇超 Zhang Qiwen;Yang Yongchao(School of Computer&Communication,Lanzhou University of Technology,Lanzhou 730050,China)
机构地区:[1]兰州理工大学计算机与通信学院,兰州730050
出 处:《计算机应用研究》2022年第2期367-373,378,共8页Application Research of Computers
基 金:国家自然科学基金资助项目(62063021)。
摘 要:针对生物地理学优化算法(biogeography-based optimization,BBO)易早熟收敛、陷入局部最优的问题,引入物种演化理论提出了改进生物地理学优化算法。该算法将所有栖息地按照物种数量划分为三种地区,并建立协同进化关系,合理地采用区间入侵、区内合作/竞争策略,满足多样性的同时避免了早熟收敛。定义了物种更迭和物种进化两种变异策略,提出的双策略协同变异算子旨在解决变异算子对较优解的破坏。通过CEC2017中的八个基准测试函数与标准BBO及相关改进算法相比,该算法在算法性能、稳定性等方面优于BBO及其他改进算法,且该算法不易被局部最优值所限制。将该算法应用于以最大完工时间为目标的柔性作业车间调度问题(flexible Job-Shop scheduling problem,FJSP)以检验其实际应用价值,实验表明,该算法在解决FJSP上具有一定的有效性。In order to solve the problem of premature convergence and falling into local optimum in biogeography-based optimization algorithm,this paper proposed an improved biogeography-based optimization algorithm introducing species evolution(SEBBO).The algorithm divided all habitats into three regions according to the number of species,established a co-evolution relationship,and reasonably adopted interval invasion and regional cooperation/competition strategies to meet diversity while avoided premature convergence.It defined two mutation strategies of species change and species development,and designed the dual-strategy cooperative mutation operator to solve the damage of the mutation operator to the better solution.Then,compared with standard BBO and other related improved algorithms on 8 benchmark functions from CEC2017,SEBBO algorithm is superior to BBO and other BBO variants in terms of algorithm performance and stability,and local optimal values is not easily limit the algorithm.Finally,this paper applied the SEBBO algorithm to flexible Job-Shop scheduling with maximum completion time as the goal to test its practical application value.Experiments show that the algorithm has a certain effectiveness in solving FJSP.
关 键 词:生物地理学优化算法 物种演化 物种更迭 柔性作业车间调度问题
分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]
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