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Atom search optimization (ASO) is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The paramount challenge in ASO is that it is prone to stagnation in local optima and premature convergence. To solve these issues, this paper uses a cellular automata structure, a Lévy flight and adaptive weight strategies to help ASO balance exploration and exploitation; the proposed method is named CALFASO. First, the cellular automata structure provides more information exchange for the population and enriches the diversity of the population. Second, a Lévy flight has the characteristics of a random walk, its short-step long-term search can improve the global search capability of the algorithm, and its short-term long-step mutations can help the algorithm jump out of local optima. Third, adaptive weights can effectively improve convergence performance. Finally, the proposed CALFASO was evaluated on the CEC2017 test set and real-world engineering problems and compared with classical and novel evolutionary algorithms to verify its performance. The experimental results and statistical analysis show that the performance of the proposed CALFASO algorithm is better than that of the other selected algorithms.

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