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首页> 外文期刊>International Journal of Swarm Intelligence and Evolutionary Computation >Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions
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Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

机译：Grasshopper优化算法在有约束和无约束测试函数中的应用

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摘要

Grasshopper Optimization algorithm is one of the recent algorithm for optimization. This algorithm is swarm based nature inspired algorithm which mimics and mathematically models the behaviour of grasshopper swarm in nature. The proposed algorithm can be used for solving the engineering optimization problems. The GOA is tested for different benchmark test functions to validate and verify the performance of the algorithm. Results obtained from GOA are compared with actual values (results) of the test functions. The results obtained from algorithm show that the algorithm is able to give the accurate results. The unconstrained and constrained test functions solved by using the Grasshopper optimization Algorithm (GOA) and the results can validate that the algorithm gives the trustable results. Constraints handling technique is used to convert the constrained optimization problem into unconstrained optimization problem, so that the problem can be handled by the Grasshopper Optimization Algorithm (GOA). Static penalty method is used as a constraints handling technique in this paper. The algorithm can also apply for different engineering problems in real life.
机译：蚱hopper优化算法是最近的优化算法之一。该算法是基于群体的自然启发算法，该算法在自然界中模拟和数学化了蚱sw群体的行为。该算法可用于解决工程优化问题。 GOA已针对不同的基准测试功能进行了测试，以验证和验证算法的性能。将GOA获得的结果与测试功能的实际值（结果）进行比较。从算法获得的结果表明，该算法能够给出准确的结果。通过使用Grasshopper优化算法（GOA）解决了无约束和受约束的测试函数，其结果可以验证该算法给出了可信赖的结果。约束处理技术用于将约束优化问题转换为无约束优化问题，以便可以通过Grasshopper优化算法（GOA）处理该问题。本文采用静态惩罚方法作为约束处理技术。该算法还可以应用于现实生活中的各种工程问题。

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