In this paper, a novel swarm‐based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank‐sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms.

The Manta ray foraging optimization (MRFO) is a novel swarm-based metaheuristic optimizer. It is mainly modeled by simulating three foraging behaviors of the Manta rays, which has a good performance. However, several drawbacks of MRFO have been noticed by analyzing its mathematical model. Random selection of reference points in the early iterations weakens the exploitation capability of MRFO. Chain foraging tends to lead the algorithm into local optimum. In addition, the algorithm suffers from the deficiency of decreasing population diversity in the late iteration. To address these shortcomings, a modified MRFO using three strategies, called m-MRFO, is proposed in this paper. An elite search pool (ESP) is established in this paper to enhance exploitation capability. By using adaptive control parameter strategies (ACP), we expand the range of MRFO’s exploration in the early iterations and enhance the accuracy of exploitation in the later iterations, balancing exploiting and exploring capabilities. Furthermore, we use a distribution estimation strategy (DES) to adjust the evolutionary direction using the dominant population information to promote convergence. The m-MRFO performance was verified by selecting 23 classical test functions and CEC2017 test suite. The significance of the results was also verified by Friedman test, Wilcoxon test and Iman-Davenport test. Moreover, we have confirmed the potential of m-MRFO to solve real-world problems by solving three engineering design problems. The simulation results show that the improvement strategy proposed in this paper can effectively improve the performance of MRFO. m-MRFO is highly competitive.

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.

Autonomous air combat technology of unmanned combat air vehicles (UCAVs) is a hot issue that is currently being studied by various countries, and maneuvering trajectory prediction is an important part of autonomous air combat research. To address the difficulty of maintaining high prediction accuracy and short prediction time simultaneously in maneuvering trajectory prediction, this paper proposes a maneuvering trajectory prediction method that is based on a layered strategy, which combines long-term maneuvering unit prediction and short-term maneuvering trajectory prediction. In long-term maneuvering unit prediction, the complex trajectory is divided into 21 types of maneuvering units using the four characteristics of maneuvering trajectories, and a maneuvering unit library is established. On the basis of the deep echo state network(DeepESN), to capture multiscale prediction input parameters, autoencoder (AE) technology is incorporated. In addition, to increase the prediction accuracy, adaptive boosting (Ada) learning technology is utilized to build a strong predictor, and seven prediction networks are compared. The results demonstrate that the proposed method realizes the highest prediction accuracy. The single-step prediction time is about 0.002 s, which meets the time requirement. In short-term maneuvering trajectory prediction, the long and short-term memory (LSTM) network is analyzed, and the gaussian random walk strategy particle swarm optimization (GWSPSO) algorithm is used to update the internal weights and biases of the network to overcome the problems of “gradient disappearance” and “gradient explosion”, and a data sharing method is proposed for overcoming the no directionality of optimization algorithms. Compared with four traditional networks, the results demonstrate the method that is proposed in this paper performs better. Compared with the sampling time of 0.3 s, the short-term prediction time of 0.05 s can also meet the requirements. Finally, a long- and short-term layered prediction method is used on a group of complex maneuvering trajectories. The results demonstrate that the prediction accuracy is significantly increased and the real-time requirements are satisfied.

The butterfly optimization algorithm (BOA) is a swarm‐based metaheuristic algorithm inspired by the foraging behaviour and information sharing of butterflies. BOA has been applied to various fields of optimization problems due to its performance. However, BOA also suffers from drawbacks such as diminished population diversity and the tendency to get trapped in local optimum. In this paper, a hybrid butterfly optimization algorithm based on a Gaussian distribution estimation strategy, called GDEBOA, is proposed. A Gaussian distribution estimation strategy is used to sample dominant population information and thus modify the evolutionary direction of butterfly populations, improving the exploitation and exploration capabilities of the algorithm. To evaluate the superiority of the proposed algorithm, GDEBOA was compared with six state‐of‐the‐art algorithms in CEC2017. In addition, GDEBOA was employed to solve the UAV path planning problem. The simulation results show that GDEBOA is highly competitive.

In this paper, a novel Adaptive Parameter Strategy Differential Evolution (APSDE) algorithm is proposed to overcome the parameters dependence and avoid local optima. The Parameter Update Mechanism (PUM), which has three different strategies, is used to reduce the dependence on parameters of DE. The Adaptive Proportion Adjustment Mechanism (APAM) is used to balance the proportion of PUM strategies in different development terms of exploitation and exploration, and the Random Restart Mechanism (RRM) is used to improve population diversity when exploitation is in stagnation. The proposed algorithm is verified in the CEC2018 test functions and the results show that APSDE has good abilities of exploitation, exploration, convergence, and stability. Secondly, Midcourse Guidance Maneuver Decision-making (MGMD) in Beyond Visual Range (BVR) air combat is studied and transformed into a single objective variational optimization problem, a MGMD system based on APSDE is established. Finally, the simulation of MGMD is carried out. The APSDE ranks first in the typical MGMD scenario experiment. In the adaptive Midcourse guidance confrontation, the winning rate of APSDE is 54%, and the statistical results show that the APSDE has an excellent MGMD ability.