Optimizing the high computational real-world problems is a challenging task that has taken a great deal of efforts in the last decade. The meta-heuristic algorithms have brought countless benefits. As a result, numerous meta-heuristic algorithms have been developed by getting inspired from natural phenomena. The atom search optimization (ASO) is a physics-based meta-heuristic, which has been developed little while ago. Although ASO is capable of solving various problems, due to low convergence speed and lack of proper balance between exploration and exploitation, it is not efficient enough in sorting out real-world convoluted problems. In the present paper, the convergence speed of ASO is improved using chaotic maps and Levy flight random walk. In addition, ASO is hybridized with the tree-seed algorithm (TSA) to improve exploration and exploitation capabilities and make a proper balance between them. TSA is an innovative intelligent meta-heuristic algorithm that has been inspired by the growth of trees and spreading their seeds and has a decent exploration ability. Furthermore, a novel technique has been applied on the proposed hybrid algorithm as a solution for departure of local optimums. Besides, the effectiveness of our contributions is validated by testing the proposed hybrid algorithm on a vast set of benchmark functions comprising unimodal, multimodal, fixed dimension, shifted–rotated and composite. The obtained results have been compared with several other new and powerful meta-heuristic algorithms in terms of descriptive and inferential statistics. In addition, the algorithms are tested on seven real-life engineering problems. The results of the experiments indicated the effectiveness of contributions and the superiority of the proposed hybrid algorithm over its akin counterparts.

中文翻译：

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一种新的基于树种子算法和 Levy 飞行的混合混沌原子搜索优化求解优化问题

优化高计算的现实世界问题是一项具有挑战性的任务，在过去十年中付出了大量努力。元启发式算法带来了无数的好处。因此，从自然现象中得到启发，开发了许多元启发式算法。原子搜索优化 (ASO) 是一种基于物理学的元启发式算法，不久前才被开发出来。ASO虽然能够解决各种问题，但由于收敛速度慢，探索与开发之间缺乏适当的平衡，在解决现实世界的复杂问题方面效率不够高。在本文中，使用混沌图和 Levy 飞行随机游走来提高 ASO 的收敛速度。此外，ASO 与树种子算法 (TSA) 相结合，以提高探索和开发能力并在它们之间取得适当的平衡。TSA是一种创新的智能元启发式算法，其灵感来自树木的生长和种子的传播，具有不错的探索能力。此外，一种新技术已应用于所提出的混合算法，作为局部最优解的解决方案。此外，通过在包括单峰、多峰、固定维度、移位旋转和复合在内的大量基准函数上测试所提出的混合算法，验证了我们贡献的有效性。在描述性和推理性统计方面，将获得的结果与其他几种新的强大的元启发式算法进行了比较。此外，这些算法在七个现实生活中的工程问题上进行了测试。实验结果表明了所提出的混合算法的有效性和优于其同类算法的优越性。