基于静态和动态罚函数法的机械零件约束群体智能设计
Constrained Cohort Intelligence using Static and Dynamic Penalty Function Approach for Mechanical Components Design
摘要：
大部分的元分析方法可以有效地解决无约束问题，但是，它们的性能可能会退化，如果涉及的约束。本文提出了两种约束处理方法的新兴元启发式队列智能（CI）。更具体地，提出了静态罚函数法（SCI）和动态罚函数法（DCI）的CI。该方法已通过解决几个约束测试问题进行了测试。并与GA、PSO、ABC、d-Ds等算法进行了性能比较。此外，以及三个现实世界的问题，从机械工程领域与改进的解决方案。结果令人满意，并验证了CI方法在解决现实世界问题的适用性。
英文摘要：
Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
