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标准的群搜索优化算法(GSO)是一种新的群智能优化算法,适用于解决高维函数的优化问题,而且简单高效,易于实现,但在其优化的后期容易陷入局部最优.为进一步提高其收敛速度和精度,对GSO算法进行了改进.保留其“发现者-加入者”模型,针对GSO算法发现者和游荡者搜索的无目的性,引进最大下降方向和杂交策略,发现者按角度搜索的同时也按最大下降方向进行搜索,游荡者通过基因突变策略的方式生成.通过23个基准测试函数对GSO算法和改进的GSO算法进行测试,结果表明改进的GSO算法在收敛速度和收敛精度上优于标准GSO算法.
The standard group search optimization algorithm (GSO) is a new swarm intelligence optimization algorithm, which is suitable for solving the optimization problems of high-dimensional functions, and is simple, efficient and easy to implement, but it tends to fall into local optimum in the late stage of optimization. Improve its convergence rate and accuracy, and improve the GSO algorithm.With its “Discoverer-Admixture” model, aiming at the purposelessness of GSO algorithm finder and rodent search, the paper introduces the direction of maximum descent and hybridization strategy and finds that The search by angle and the direction of the maximum descent, the rodents generated by gene mutation strategy.The GSO algorithm and the improved GSO algorithm were tested by 23 benchmark test functions, the results show that the improved GSO algorithm in the convergence rate And the convergence accuracy is superior to the standard GSO algorithm.