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.

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.

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.

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, an autonomous UCAV air combat maneuvering decision method based on LSHADE-TSO optimization in a model predictive control framework is proposed, along with enemy trajectory prediction. First, a sliding window recursive prediction method for multi-step enemy trajectory prediction using a Bi-LSTM network is proposed. Second, Model Predictive Control (MPC) theory is introduced, and when combined with enemy trajectory prediction, a UCAV maneuver decision model based on the MPC framework is proposed. The LSHADE-TSO algorithm is proposed by combining the LSHADE and TSO algorithms, which overcomes the problem of traditional sequential quadratic programming falling into local optimum when solving complex nonlinear models. The LSHADE-TSO-MPC air combat maneuver decision method is then proposed, which combines the LSHADE-TSO algorithm with the MPC framework and employs the LSHADE-TSO algorithm as the optimal control sequence solver. To validate the effectiveness of the maneuvering decision method proposed in this paper, it is tested against the test maneuver and the LSHADE-TSO decision algorithm, respectively, and the experimental results show that the maneuvering decision method proposed in this paper can beat the opponent and win the air combat using the same weapons and flight platform. Finally, to demonstrate that LSHADE-TSO can better exploit the decision-making ability of the MPC model, LSHADE-TSO is compared to various optimization algorithms based on the MPC model, and the results show that LSHADE-TSO-MPC can not only help obtain air combat victory faster but also demonstrates better decision-making ability.