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The self-organizing worm algorithm
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作者 Zheng Gaofei Wang Xiufeng Zhang Yanli 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第3期650-654,共5页
A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows: dispers... A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows: disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions. 展开更多
关键词 control theory multi-modal optimization algorithm self-organizing worm algorithm unit
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带子群的自组织蠕虫算法在优化方面的应用 被引量:1
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作者 郑高飞 王秀峰 《计算机工程与应用》 CSCD 北大核心 2006年第35期21-23,29,共4页
带子群的自组织蠕虫算法(Subgroup-Self-OrganizingWormAlgorithm,SSOMA)是一种全新的基于涌现方法的多模态优化算法。与传统的多模态算法相比,该算法具有计算简单、收敛性好、精度高且不需要任何先验知识等优点。对该算法在高维多模态... 带子群的自组织蠕虫算法(Subgroup-Self-OrganizingWormAlgorithm,SSOMA)是一种全新的基于涌现方法的多模态优化算法。与传统的多模态算法相比,该算法具有计算简单、收敛性好、精度高且不需要任何先验知识等优点。对该算法在高维多模态问题优化方面的应用进行了一定的探索,提出了适用于高维函数的算法,用经典测试函数对该算法进行了仿真实验,并进行了计算复杂度分析,结果表明该算法在高维多模态函数优化方面具有较为理想的应用前景。 展开更多
关键词 子群 自组织蠕虫算法 多模态优化算法 多维函数 涌现
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