LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems

A. W. Mohamed, Anas A. Hadi, A. Fattouh, K. Jambi
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引用次数: 223

Abstract

To improve the optimization performance of LSHADE algorithm, an alternative adaptation approach for the selection of control parameters is proposed. The proposed algorithm, named LSHADE-SPA, uses a new semi-parameter adaptation approach to effectively adapt the values of the scaling factor of the Differential evolution algorithm. The proposed approach consists of two different settings for two control parameters F and Cr. The benefit of this approach is to prove that the semi-adaptive algorithm is better than pure random algorithm or fully adaptive or self-adaptive algorithm. To enhance the performance of our algorithm, we also introduced a hybridization framework named LSHADE-SPACMA between LSHADE-SPA and a modified version of CMA-ES. The modified version of CMA-ES undergoes the crossover operation to improve the exploration capability of the proposed framework. In LSHADE-SPACMA both algorithms will work simultaneously on the same population, but more populations will be assigned gradually to the better performance algorithm. In order to verify and analyze the performance of both LSHADE-SPA and LSHADE-SPACMA, Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with LSHADE algorithm are executed. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, of both LSHADE-SPA and LSHADE-SPACMA are better than LSHADE algorithm, especially as the dimension increases.
半参数自适应LSHADE与CMA-ES混合求解CEC 2017基准问题
为了提高LSHADE算法的优化性能,提出了一种替代自适应方法来选择控制参数。提出的LSHADE-SPA算法采用一种新的半参数自适应方法,对差分进化算法的比例因子值进行有效的自适应。该方法由两个控制参数F和Cr的两种不同设置组成。该方法的优点是证明了半自适应算法优于纯随机算法或完全自适应或自适应算法。为了提高算法的性能,我们还在LSHADE-SPA和改进版CMA-ES之间引入了一个名为LSHADE-SPACMA的杂交框架。改进后的CMA-ES进行了交叉操作,提高了框架的勘探能力。在LSHADE-SPACMA中,两种算法将同时处理相同的种群,但更多的种群将逐渐分配给性能更好的算法。为了验证和分析LSHADE- spa和LSHADE- spacma的性能,在CEC2017基准测试中对10、30、50和100个维度的30个测试问题进行了数值实验,并与LSHADE算法进行了比较。实验结果表明,LSHADE- spa和LSHADE- spacma算法在鲁棒性、稳定性和得到的解的质量上都优于LSHADE算法,尤其是当维数增加时。
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