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.

In this paper, inspired by the T cell immune process, a new meta-heuristic algorithm named T cell immune algorithm (TCIA) is proposed to better solve various complex practical problems. The core difficulty of meta-heuristic algorithms is balancing exploration and exploitation. TCIA mimics the behavior of T-cell immune process in recognizing antigens, activating cells and attacking pathogens, thus to better balance exploration and exploitation. Recognition is the process of judging the cell concentration at the current location, which is used to evaluate the fitness value of the current cell. Activation is the process by which T cells are activated and thus differentiate into multiple cells, helping the population to explore on a larger scope. Attacks are divided into random attack and directed attack. Random attack helps the population escape from local optima by randomly selecting a target cell to attack. Targeted attacks push the overall approach to the location with the highest concentration of target cells, which makes better use of the global optimal solution. The TCIA is tested on CEC2022 and compared with other 10 algorithms. Experimental results and statistical analysis show that TCIA is more effective than other 10 classical and new meta-heuristic algorithms in solving constraint function problems. TCIA is tested on three multi-objective functions, which verifies its good ability in solving multi-objective functions. Two classical engineering design problems are applied to verify the ability of TCIA to solve practical problems. The parameters of Back propagation neural network are optimized by TCIA to predict the compressive strength of concrete. The results show that the ability of TCIA to solve a variety of problems is balanced and excellent.