摘要
针对果蝇优化算法的早熟收敛问题,提出一种基于最优和最差个体协同学习的果蝇优化算法。该算法通过在进化方程中添加向最差个体学习的改进策略,优化进化方程,增强算法跳出局部最优、寻找全局最优的能力。对经典测试函数的仿真结果表明,该算法具有更好的全局搜索能力,在收敛速度、收敛可靠性及收敛精度上比其他算法有较大的提高。
Considering the premature convergence problem of Fruit fly Optimization Algorithm（FOA）, a new collaborative learning FOA based on the best and the worst individual is presented. The evolutionary equation is optimized by adding learning the worst individual to it. The ability of the algorithm to break away from the local optimum and to find the global optimum is greatly enhanced. Experimental resut~ show that the new algorithm has the advantages of better global search ability, speeder convergence and more precise convergence.
出处
《计算机工程》
CAS
CSCD
2013年第11期223-225,239,共4页
Computer Engineering
基金
国家自然科学基金资助项目(61063028)
甘肃省科技支撑计划基金资助项目(1011NKCA058)
甘肃省教育厅科研基金资助项目(1202-04)
甘肃省高等学校科研基金资助项目(2013A-060)
关键词
果蝇优化
群体智能
反向认知
协同学习
优化进化方程
收敛精度
fruit fly optimization
swarm intelligence
reverse cognition
collaborative learning
optimization evolution equation
convergence precision
