The concise concept and good optimization performance are the advantages of particle swarm optimization algorithm (PSO), which makes it widely used in many fields. However, when solving complex multimodal optimization problems, it is easy to fall into early convergence. The rapid loss of population diversity is one of the important reasons why the PSO algorithm falls into early convergence. For this reason, this paper attempts to combine the PSO algorithm with wavelet theory and levy flight theory to propose a new hybrid algorithm called PSOLFWM. It applies the random wandering of levy flight and the mutation operation of wavelet theory to enhance the population diversity and seeking performance of the PSO to make it search more efficiently in the solution space to obtain higher quality solutions. A series of classical test functions and 19 optimization algorithms proposed in recent years are used to evaluate the optimization performance accuracy of the proposed method. The experimental results show that the proposed algorithm is superior to the comparison method in terms of convergence speed and convergence accuracy. The success of the high-dimensional function test and dynamic shift performance test further verifies that the proposed algorithm has higher search stability and anti-interference performance than the comparison algorithm. More importantly, both t-Test and Wilcoxon’s rank sum test statistical analyses were carried out. The results show that there are significant differences between the proposed algorithm and other comparison algorithms at the significance level α = 0.05, and the performance is better than other comparison algorithms.

No abstract

No abstract

It is important to study the water surface infrared characteristics caused by the hot wake of the underwater vehicle from the perspective of infrared radiation for submarine detection. At present, most of the experimental water environments on surface infrared characteristics are set up as uniform environments or ideal linear temperature stratified environments. However, the temperature distribution environment of the real sea is complex and diverse, and further improvement of the water environment is still needed to obtain more realistic results. Therefore, this paper constructs a nonlinear temperature stratified environment that conforms to the real water environment, and conducts numerical simulations and experimental validation in this environment to observe the infrared characteristics of the water surface. The results show that in the nonlinear temperature stratified environment, the submarine will produce cold wake phenomenon. Compared with the uniform temperature environment in the same working condition, the wake is longer and less likely to dissipate, and the surface infrared characteristics can be detected more obviously in the non-linear.