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
In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of trackin...In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of tracking the global maximum power point(GMPP)under partial shading condition(PSC).The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points(MPPs).The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT.The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits.In general,all of the AI-based MPPT techniques exhibit fast convergence speed,less steady-state oscillation and high efficiency,compared with the conventional MPPT techniques.However,the AI-based MPPT techniques are computationally intensive and costly to realize.Overall,the hybrid MPPT is favorable in terms of the balance between performance and complexity,and it combines the advantages of conventional and AI-based MPPT techniques.In this paper,a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results.The merits,open issues and technical implementations of AI-based MPPT techniques are evaluated.We intend to provide new insights into the choice of optimal AI-based MPPT techniques.展开更多
灰狼优化(grey wolf optimization,GWO)算法是模拟灰狼的种群活动而提出的群智能算法,该算法因其在高维度的求解精度较高而受到广泛关注,但是它与其他群智能算法一样存在收敛慢和易陷入局部最优的缺点。针对GWO算法所存在的问题,文章基...灰狼优化(grey wolf optimization,GWO)算法是模拟灰狼的种群活动而提出的群智能算法,该算法因其在高维度的求解精度较高而受到广泛关注,但是它与其他群智能算法一样存在收敛慢和易陷入局部最优的缺点。针对GWO算法所存在的问题,文章基于非线性控制因子和遗传算法中的变异思想,提出了一种改进的基于非线性控制因子和遗传变异的GWO算法(grey wolf optimization algorithm based on the nonlinear control factor and genetic variation,NGGWO),并提出一种基于余弦变换的非线性收敛因子,用于平衡算法的全局与局部搜索能力;同时,在算法中引入遗传变异策略,用于解决算法陷入局部时的停滞现象;通过一组基准测试函数,将NGGWO与GWO和其改进算法进行比较。实验结果表明,NGGWO基本优于GWO算法,相比于该文提出的3种改进GWO算法,NGGWO也具有性能上的优势。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 School of EngineeringMonash University Malaysia
文摘In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of tracking the global maximum power point(GMPP)under partial shading condition(PSC).The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points(MPPs).The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT.The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits.In general,all of the AI-based MPPT techniques exhibit fast convergence speed,less steady-state oscillation and high efficiency,compared with the conventional MPPT techniques.However,the AI-based MPPT techniques are computationally intensive and costly to realize.Overall,the hybrid MPPT is favorable in terms of the balance between performance and complexity,and it combines the advantages of conventional and AI-based MPPT techniques.In this paper,a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results.The merits,open issues and technical implementations of AI-based MPPT techniques are evaluated.We intend to provide new insights into the choice of optimal AI-based MPPT techniques.
文摘灰狼优化(grey wolf optimization,GWO)算法是模拟灰狼的种群活动而提出的群智能算法,该算法因其在高维度的求解精度较高而受到广泛关注,但是它与其他群智能算法一样存在收敛慢和易陷入局部最优的缺点。针对GWO算法所存在的问题,文章基于非线性控制因子和遗传算法中的变异思想,提出了一种改进的基于非线性控制因子和遗传变异的GWO算法(grey wolf optimization algorithm based on the nonlinear control factor and genetic variation,NGGWO),并提出一种基于余弦变换的非线性收敛因子,用于平衡算法的全局与局部搜索能力;同时,在算法中引入遗传变异策略,用于解决算法陷入局部时的停滞现象;通过一组基准测试函数,将NGGWO与GWO和其改进算法进行比较。实验结果表明,NGGWO基本优于GWO算法,相比于该文提出的3种改进GWO算法,NGGWO也具有性能上的优势。
