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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. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments. • A Q-learning based multi-strategy integrated ABC algorithm is proposed. • Multiple strategies are introduced to the onlooker bee phase. • Q-learning is utilized to adaptively determine the most suitable strategy. • Novel state and action settings are designed in Q-learning framework. • Proposed algorithm shows superiority in numerical and practical experiments. [ABSTRACT FROM AUTHOR]