The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe...The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.展开更多
针对捕鱼优化算法(catch fish optimization algorithm,CFOA)容易陷入局部最优、迭代后期种群多样性单一等问题,提出一种多策略融合改进的捕鱼优化算法。首先,通过反向学习策略进行种群初始化,以提高初始种群的质量;其次,引入组长趋同...针对捕鱼优化算法(catch fish optimization algorithm,CFOA)容易陷入局部最优、迭代后期种群多样性单一等问题,提出一种多策略融合改进的捕鱼优化算法。首先,通过反向学习策略进行种群初始化,以提高初始种群的质量;其次,引入组长趋同自适应组队策略,强化算法优势经验的学习;最后,通过引入Lévy飞行螺旋搜索策略,改善集体捕获阶段算法跳出局部最优值的能力;改进算法与灰狼优化(grey wolf optimization,GWO)算法、麻雀优化算法(sparrow search algorithm,SSA)、鲸鱼优化算法(whale optimization algorithm,WOA)、正弦余弦优化算法(sine cosine algorithm,SCA)等7种算法在15个基准测试函数上进行了仿真对比分析。试验结果表明,改进算法在求解精度和收敛速度等方面有较好提升。此外,3个工程设计优化问题的仿真试验进一步验证了改进算法在处理工程优化问题上的优越性。展开更多
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.展开更多
地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算...地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.展开更多
基金supported by the National Natural Science Foundation of China(61271250)
文摘The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.
基金supported by the National Key Research and Development Program of China(2016YFD0700605)the Fundamental Research Funds for the Central Universities(JZ2016HGBZ1035)the Anhui University Natural Science Research Project(KJ2017A891)
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
文摘地震反演技术能够最有效地从地震信号中挖掘地层参数和岩性信息,一直是储层预测研究的焦点.传统线性地震反演算法缺乏全局搜索能力,反演结果精度较低.本研究以全局寻优为出发点,将一种结构简单和寻优能力强的全局优化算法——梯度优化算法(Gradient-Based Optimizer,GBO),引入地震反演.相比于差分进化等其他全局优化算法,GBO算法通过梯度随机搜索机制和局部逃逸算子进行全局搜索,能有效降低地震反演的多解性.但是,GBO算法收敛速度慢和局部随机性强,难以满足大批量的地震反演计算需求.因此,本文在GBO算法迭代过程中引入Wolfe线性局部搜索机制,提出基于Wolfe搜索的随机梯度优化算法(Stochastic—Gradient Optimization Based on Wolfe's Search,SGO-WS).在全局搜索过程中,通过线性搜索算子,充分挖掘当前迭代解周围的局部最优,既保证了反演解精度,又大幅提高了原GBO算法的计算效率,同时还有效降低了反演解的局部随机性.Marmousi-2模型测试验证了SGO-WS算法的可行性和准确性,厄瓜多尔Tapir油田地震资料也验证了SGO-WS算法的实用性.
