...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert systems with applications >Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems
【24h】

Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems

机译：Chameleon Swarm算法：一种用于解决工程设计问题的生物启发优化器

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360 degrees scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixtyseven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.
机译：本文提出了一种名为Chameleon Sharm算法（CSA）的新型元 - 启发式算法，用于解决全局数值优化问题。 CSA的基础灵感是Chameleons在树木，沙漠和沼泽附近的食物来源进行导航和狩猎时的动态行为。该算法在数学上模型和实现了Chameleons的搜索中的行为步骤，包括它们在将目光旋转到近360度的视觉方面，以使用高速发射的粘性舌头定位猎物和抓取猎物的行为。当施用以解决优化问题时，变色齿轮练习的这些觅食机制最终导致可行的解决方案。在SixtySeven基准测试功能中评估了所提出的算法的稳定性，并使用几种评估措施检查了性能。这些测试功能涉及单向，多式联运，混合动力车和组成功能，具有不同程度的复杂性。进行了广泛的比较研究，以证明CSA在优化准确性方面对其他元启发式算法的功效。在解决五个受约束和计算昂贵的工程设计问题时，表明了所提出的算法在可靠地解决现实问题中的适用性。 CSA的总体结果表明，与其他元启发式有关的全球或近全球解决方案和更好的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号