A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design
A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design
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DOI：
10.1007/s11081-016-9343-0
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发表时间：
2016-11
影响因子：
2.1
通讯作者：
D. Poole;C. Allen;T. Rendall
中科院分区：
文献类型：
--
作者：
D. Poole;C. Allen;T. Rendall
提出了一个可与任何基于群的优化算法一起使用的通用约束处理框架。为了使群优化算法有效地解决约束优化问题，必须对优化算法进行修改以处理约束，然而，这些约束处理框架通常并不普遍适用于所有群算法。因此，本文提出了一个约束处理框架，它与任何群优化算法兼容，这样用户就可以将其包裹在一个选定的群算法和执行约束优化。该方法称为分离子群，其工作原理是根据个体智能体的可行性对种群进行划分。这使得所有可行的代理移动现有的群优化算法，从而促进了良好的性能和收敛特性的个体群算法。该框架进行了测试的一套分析测试功能和一些工程基准问题，并与其他通用的约束处理框架相比，使用四种不同的群优化;粒子群，引力搜索，混合算法和差分进化。结果表明，新的框架产生上级的结果相比，所有四个群算法测试的既定框架。最后，将该框架应用于气动外形优化设计问题，获得了无激波解。
A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers; particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.