Slime mould algorithm (SMA) is a population‐based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition‐based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed‐dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real‐world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

As optimization problems become more complex, the need of new algorithms becomes greater. In this paper, a new powerful algorithm based on nuclear reaction process, namely, nuclear reaction optimization, is proposed for numerical optimization. The NRO algorithm which includes nuclear fission and fusion phases mimics the fission and fusion processes. In the first phase, the Gaussian walk and differential evolution operators are used for modeling fission process. In the second phase, the variants of differential evolution operators are employed for modeling the ionization stage and fusion stage. Additionally, six benchmark functions and unconstrained engineering design problem are evaluated by NRO and other compared algorithms. The optimization results indicate that the NRO has presented feasibility and superiority performance.