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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]

Nuclear power plant workers inevitably work in a radiation environment. Minimum dose path planning becomes an important issue in ensuring that they receive as little radiation as possible. However, a work environment with complex obstacles or narrow passages makes the problem of finding the path with the minimum dose more difficult. In this paper, we propose a meta-heuristic method to solve this problem. An algorithm called Rao-combined artificial bee colony (RABC) is proposed with a novel obstacle detour technique. The RABC algorithm combines the directional solution update method of the Rao algorithm with the traditional ABC algorithm to further improve performance. To evaluate the effectiveness of the proposed method, three hypothetical environments are simulated. In the first hypothetical environment where there are no obstacles and many radiation sources are scattered, the meta-heuristic algorithms, PSO, ABC and RABC are compared, and in the second hypothetical environment with narrow passages and obstacles, the grid-based A* algorithm and the sampling-based RRT* algorithm are used for comparison with RABC. The third hypothetical environment is a complex environment where many radiation sources and obstacles are distributed and there are narrow passages. In this case, the proposed algorithm is compared with the Dijkstra algorithm and the GB-RRT* algorithm, which is known as one of the best in obstacle avoidance. Experimental results show that the proposed method is superior in exploration and exploitation, convergence and robustness compared to other meta-heuristic algorithms as well as grid-based or sampling-based algorithms. • The minimum dose path planning problem is solved by meta-heuristic method. • A novel obstacle detour technique is developed. • Rao-combined artificial bee colony (RABC) algorithm is proposed. [ABSTRACT FROM AUTHOR]

This paper aims to propose a novel design approach for on-line path planning of the multiple mobile robots system with free collision. Based on the artificial bee colony (ABC) algorithm, we propose an efficient artificial bee colony (EABC) algorithm for solving the on-line path planning of multiple mobile robots by choosing the proper objective function for target, obstacles, and robots collision avoidance. The proposed EABC algorithm enhances the performance by using elite individuals for preserving good evolution, the solution sharing provides a proper direction for searching, the instant update strategy provides the newest information of solution. By the proposed approach, the next positions of each robot are designed. Thus, the mobiles robots can travel to the designed targets without collision. Finally, simulation results of illustration examples are introduced to show the effectiveness and performance of the proposed approach. [ABSTRACT FROM AUTHOR]