The Meta-heuristic algorithm has become an effective solution to global optimization problems. Recently, a new meta-heuristic algorithm called sine-cosine algorithm (SCA) search algorithm is proposed, which uses the characteristics of sine-cosine trigonometric function in mathematical formulas to solve the optimal solution of the problem to be optimized. This paper presents a new variant of the SCA algorithm named Bare bones Sine Cosine Algorithm (BBSCA), which improves the exploitation ability of the solution, reduces the diversity spillover in the classical SCA search equation, and keeps the diversity of the solution very well. The proposed method uses Gaussian search equations and exponential decrement strategies to generate new candidate individuals, which use the valuable information hidden in the best individuals to guide the population to move in a better direction. At the same time, the greedy selection mechanism is adopted for the newly generated solution, which makes full use of the previously searched information to improve the individual's search ability. To evaluate the effectiveness in solving the global optimization problems, BBSCA has been tested on classic set of 23 well-known benchmark functions, standard IEEE CEC2014 and CEC2017 benchmark functions, and compared with several other state-of-the-art SCA algorithm variants. At the end of the paper, the performance of design algorithm BBSCA is also tested on classical engineering optimization problems. The numerical and simulation experimental results indicate that the proposed method can improve the performance of the algorithm and generate better statistical significance solutions in real-life global optimization problems.

元启发式算法已经成为解决全局优化问题的有效方法。近年来，提出了一种新的元启发式算法，称为正弦余弦算法（SCA）搜索算法，该算法利用数学公式中正弦余弦三角函数的特征来求解待优化问题的最优解。本文提出了一种新的SCA算法变种，称为裸骨正弦余弦算法（BBSCA），它提高了解决方案的利用能力，减少了传统SCA搜索方程式中的多样性溢出，并很好地保持了解决方案的多样性。拟议的方法使用高斯搜索方程和指数递减策略来生成新的候选个体，它利用隐藏在最佳个体中的宝贵信息来指导人们朝着更好的方向发展。同时，针对新生成的解决方案采用贪婪选择机制，该机制充分利用了先前搜索到的信息，提高了个人的搜索能力。为了评估解决全局优化问题的有效性，BBSCA已在23套经典的著名基准功能，标准IEEE CEC2014和CEC2017基准功能上进行了测试，并与其他几种最新的SCA算法变体进行了比较。最后，还针对经典工程优化问题对设计算法BBSCA的性能进行了测试。