摘要
为寻求以提高波浪发电系统的平均输出功率为目标的系统最优负载,针对遗传算法、粒子群算法等优化算法存在早熟收敛和全局搜索能力不足的问题引入人群搜索算法。该算法通过建立目标函数值与步长的关系式,根据个体当前位置的适应度值计算下一步搜索步长;采用利己、利他和预动3个方向随机加权几何平均方案,确定个体搜索方向,提高个体全局搜寻能力,使算法避免陷入局部最优解并可得到全局最优解。仿真结果表明,与传统粒子群优化算法相比,所提算法收敛速度快,可增加波浪发电系统的平均输出功率。
Aiming at the genetic algorithm and the particle swarm optimization algorithm having low probability in searching global optimization and premature convergence,the seeker optimization algorithm was proposed to seek the optimal load of the wave power generation system for increacing the average output power. The relation between target value and step length was established so that the step length of the next step was calculated according to the fitness value of the current position of the seeker. Then the seeking direction was selected by the weighted random geometric mean value of the egotistic behavior,altruistic behavior and pro-activeness behavior to enhance the global search ability of the seeker. The global optimum solution can be acquired to avoid falling into the local optimization. The simulation results show that the proposed algorithm can converge quickly and increase the average output power of the wave power generation system.
作者
杨俊华
邹子君
杨金明
王子为
Yang Junhua;Zou Zijun;Yang Jinming;Wang Ziwei(School of Automation,Guangdong University of Technology,Guangzhou 510006,China;School of Electric Power Engineering,South China University of Technology,Guangzhou 510641,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2019年第10期2725-2731,共7页
Acta Energiae Solaris Sinica
基金
国家自然科学基金（513770265）
广东省科技计划（2016B090912006）
广东省自然科学基金（2015A030313487）
广东省教育部产学研合作专项资金（2013B090500089）
关键词
波浪发电
直驱型波浪发电系统
最优算法
人群搜索算法
wave power conversion
directing wave generation system
optimization
seeker optimization algorithm(SOA)
