Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee...Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.展开更多
In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and ...In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and convergence accuracy.First,a chaotic mapping is applied to initial-ize the population in order to improve the quality of the population and thus the convergence speed of the algorithm.Second,the prey’s position is improved during the prey-hunting phase.Then,the COA is combined with the particle swarm optimization(PSO)and the golden sine algorithm(Gold-SA),and the position is updated with probabilities to avoid local extremes.Finally,a population decreasing strategy is applied as a way to improve the performance of the algorithm in a comprehen-sive approach.The paper compares the proposed algorithm MICOA with 7 well-known meta-heuristic optimization algorithms and evaluates the algorithm in 23 test functions as well as engineering appli-cation.Experimental results show that the MICOA proposed in this paper has good effectiveness and superiority,and has a strong competitiveness compared with the comparison algorithms.展开更多
In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of ...In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.展开更多
在光照不均匀或局部阴影下,光伏阵列的输出呈现多极值现象,传统的最大功率点跟踪(maximum power point tracking,MPPT)难以搜索全局最优输出功率。为了提升局部阴影下光伏阵列的发电效率,提出一种基于改进长鼻浣熊优化算法(ICOA)的PV-M...在光照不均匀或局部阴影下,光伏阵列的输出呈现多极值现象,传统的最大功率点跟踪(maximum power point tracking,MPPT)难以搜索全局最优输出功率。为了提升局部阴影下光伏阵列的发电效率,提出一种基于改进长鼻浣熊优化算法(ICOA)的PV-MPPT策略,结合立方混沌映射,以提高算法收敛速度和全局搜索能力。MATLAB/Simulink仿真结果表明,相较于传统智能算法,采用ICOA-MPPT控制策略,提高了跟踪速度和精度。展开更多
基金funded by the National Key Research and Development Program(Grant No.2022YFB3706904).
文摘Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis.
基金Supported by the National Key R&D Program of China(2022ZD0119001).
文摘In this paper,a multi-strategy improved coati optimization algorithm(MICOA)for engineering applications is proposed to improve the performance of the coati optimization algorithm(COA)in terms of convergence speed and convergence accuracy.First,a chaotic mapping is applied to initial-ize the population in order to improve the quality of the population and thus the convergence speed of the algorithm.Second,the prey’s position is improved during the prey-hunting phase.Then,the COA is combined with the particle swarm optimization(PSO)and the golden sine algorithm(Gold-SA),and the position is updated with probabilities to avoid local extremes.Finally,a population decreasing strategy is applied as a way to improve the performance of the algorithm in a comprehen-sive approach.The paper compares the proposed algorithm MICOA with 7 well-known meta-heuristic optimization algorithms and evaluates the algorithm in 23 test functions as well as engineering appli-cation.Experimental results show that the MICOA proposed in this paper has good effectiveness and superiority,and has a strong competitiveness compared with the comparison algorithms.
基金Project supported by the National Natural Science Foundation of China (No. 60474064), and the Natural Science Foundation of Zhejiang Province (No. Y105694), China
文摘In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
文摘在光照不均匀或局部阴影下,光伏阵列的输出呈现多极值现象,传统的最大功率点跟踪(maximum power point tracking,MPPT)难以搜索全局最优输出功率。为了提升局部阴影下光伏阵列的发电效率,提出一种基于改进长鼻浣熊优化算法(ICOA)的PV-MPPT策略,结合立方混沌映射,以提高算法收敛速度和全局搜索能力。MATLAB/Simulink仿真结果表明,相较于传统智能算法,采用ICOA-MPPT控制策略,提高了跟踪速度和精度。
