Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of ...Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.展开更多
为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶...为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶段增加反向学习,增强种群多样性.根据算法各个阶段不同特征引入衰减因子,平衡全局和局部勘探能力.选取8个高维函数和23个不同特征的优化函数对算法性能进行测试,进一步使用收敛性分析,寻优成功率,CPU时间,Wilcoxon秩和检验来评估改进算法,实验结果表明,ORGWO算法在求解高维问题上具有较好的精度,鲁棒性和更快的收敛速度.展开更多
为解决大规模突发灾害给人民带来的生理与心理痛楚问题,考虑模糊需求情景下灾区道路受损、物资相对短缺、灾区需求紧迫度差异等因素,同时考虑灾民有限理性下物资竞争心理,运用前景理论刻画灾民对物资分配、运抵时间的综合感知,以灾区运...为解决大规模突发灾害给人民带来的生理与心理痛楚问题,考虑模糊需求情景下灾区道路受损、物资相对短缺、灾区需求紧迫度差异等因素,同时考虑灾民有限理性下物资竞争心理,运用前景理论刻画灾民对物资分配、运抵时间的综合感知,以灾区运输时间感知满意度最大、物资分配感知损失最小、运输成本最小为目标构建应急物资调度多目标优化模型,设计改进灰狼优化算法(Grey Wolf Optimizer,GWO)求解,引入混沌反向学习、差分进化、非线性收敛等策略实现对GWO算法的改进,并以2008年四川地震案例数据展开分析验证,依据模糊逻辑加权法选择合适的应急调度方案。研究表明,该模型可合理衡量有限理性下灾民综合感知,改进算法能够得出更加公平高效的调度方案,有效解决了灾后模糊需求情景下应急物资调度问题。展开更多
文摘Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.
文摘为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶段增加反向学习,增强种群多样性.根据算法各个阶段不同特征引入衰减因子,平衡全局和局部勘探能力.选取8个高维函数和23个不同特征的优化函数对算法性能进行测试,进一步使用收敛性分析,寻优成功率,CPU时间,Wilcoxon秩和检验来评估改进算法,实验结果表明,ORGWO算法在求解高维问题上具有较好的精度,鲁棒性和更快的收敛速度.
文摘为解决大规模突发灾害给人民带来的生理与心理痛楚问题,考虑模糊需求情景下灾区道路受损、物资相对短缺、灾区需求紧迫度差异等因素,同时考虑灾民有限理性下物资竞争心理,运用前景理论刻画灾民对物资分配、运抵时间的综合感知,以灾区运输时间感知满意度最大、物资分配感知损失最小、运输成本最小为目标构建应急物资调度多目标优化模型,设计改进灰狼优化算法(Grey Wolf Optimizer,GWO)求解,引入混沌反向学习、差分进化、非线性收敛等策略实现对GWO算法的改进,并以2008年四川地震案例数据展开分析验证,依据模糊逻辑加权法选择合适的应急调度方案。研究表明,该模型可合理衡量有限理性下灾民综合感知,改进算法能够得出更加公平高效的调度方案,有效解决了灾后模糊需求情景下应急物资调度问题。
