Kepler Optimization Algorithm (KOA) is a physically based meta-heuristic algorithm inspired by Kepler's laws to simulate planetary motions, KOA shows strong performance on different test sets as well as various optimization problems. However, it also suffers from imbalanced exploration and exploitation, delayed convergence, and insufficient convergence accuracy in dealing with high-dimensional and complex applications. To address these shortcomings, this paper proposes an enhanced Kepler optimization algorithm called CGKOA with stronger performance by combining adaptive function, sinusoidal chaotic gravity, lateral crossover, and elite gold rush strategies. Firstly, the adaptive function and sinusoidal chaotic gravity are adjustments to the internal structure of KOA algorithm, which successfully balances the exploration and exploitation, and increases the population diversity. Secondly, the lateral crossover strategy strengthens the spatial exploration ability of the algorithm, eliminates the poor quality individuals and accelerate the output of high-quality population, and finally, the proposed elite gold rush strategy provides an in-depth and rational exploration of elite groups from multiple perspectives, improves solving accuracy and accelerates the convergence speed. Experimental comparisons of CGKOA with a variety of state-of-the-art and high-performance algorithms on different dimensions of the 2017 and 2020 test sets are conducted, and the experimental results show the superiority and robustness of CGKOA algorithm. In addition, the effectiveness and practicability of CGKOA for real problems are verified by solving 50 complex engineering applications. Last, the algorithm is applied to the difficult problems in the fields of path planning, job-shop scheduling, variant travelers, robot machining trajectory planning, and complex truss topology optimization, and the excellent results obtained by CGKOA demonstrate its applicability and development potential for optimization tasks in various fields. Therefore, CGKOA is an efficient and competitive algorithm for solving complex optimization problems with different dimensions.

中文翻译：

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CGKOA：针对多域优化问题的增强型开普勒优化算法

开普勒优化算法（KOA）是一种受开普勒定律启发的基于物理的元启发式算法，用于模拟行星运动，KOA在不同的测试集以及各种优化问题上表现出强大的性能。然而，它也存在探索和利用不平衡、收敛延迟以及在处理高维和复杂应用时收敛精度不足的问题。针对这些缺点，本文结合自适应函数、正弦混沌引力、横向交叉和精英淘金策略，提出了一种性能更强的增强型开普勒优化算法CGKOA。首先，自适应函数和正弦混沌引力是对KOA算法内部结构的调整，成功地平衡了探索和利用，增加了种群多样性。其次，横向交叉策略强化了算法的空间探索能力，淘汰了劣质个体，加速了优质种群的产出，最后，提出的精英淘金策略为精英群体提供了深入、理性的探索。从多个角度，提高求解精度，加快收敛速度​​。在2017年和2020年测试集的不同维度上对CGKOA与多种最先进的高性能算法进行了实验比较，实验结果表明了CGKOA算法的优越性和鲁棒性。此外，通过解决50个复杂的工程应用，验证了CGKOA对于实际问题的有效性和实用性。最后，将该算法应用于路径规划、作业车间调度、变体行走机构、机器人加工轨迹规划、复杂桁架拓扑优化等领域的疑难问题，CGKOA取得的优异结果证明了其在机器人领域的适用性和发展潜力。各个领域的优化任务。因此，CGKOA是一种解决不同维度的复杂优化问题的高效且有竞争力的算法。