With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to find the global optimal solution quickly and accurately. In order to deal with these defects of standard particle swarm optimization, this paper proposes a particle swarm optimization algorithm (SHMPSO) based on the hybrid strategy of seed swarm optimization (using codes available from https://gitee.com/mr-xie123234/code/tree/master/). In SHMPSO, a subpopulation coevolution particle swarm optimization algorithm is adopted. In SHMPSO, an elastic candidate-based strategy is used to find a candidate and realize information sharing and coevolution among populations. The mean dimension learning strategy can be used to make the population converge faster and improve the solution accuracy of SHMPSO. Twenty-one benchmark functions and six industries-recognized particle swarm optimization variants are used to verify the advantages of SHMPSO. The experimental results show that SHMPSO has good convergence speed and good robustness and can obtain high-precision solutions.
This paper proposes an elite coded particle swarm optimization (CEPSO) unmanned aerial vehicles (UAVs) search target motion for target detection in the shortest time. According to Bayesian theory, the problem of moving object searched is transformed into the optimization of cost function, which is used to describe the probability of moving objects being found. In order to better fit the problem, a coded elite particle swarm optimization algorithm is used to encode the search trajectory of the UAV as the movement path of the particle in the process of generation and evolution. This coding method uses the cognitive properties and social learning properties of particle swarms to generate better solutions. At the same time, in order to increase the diversity of particle swarms, a method of elite particle perturbation is used, which greatly improves the detection probability. In order to verify the superiority of the algorithm in this paper, a large number of simulation experiments are carried out on the existing methods. The experimental results show that compared with PSO, the detection probability of the proposed CEPSO is improved by 6.9%, and the time performance is improved by 8.5%. It also compares with other meta-heuristic algorithms, including differential evolution (DE), artificial bee colony (ABC), harmony search (HS), biogeographic-based optimization (BBO), invasive weed optimization (IWO), teaching-learning-based Optimization (TLBO). The experimental results show that the algorithm proposed in this paper has better detection accuracy and less detection time.
When traditional particle swarm optimization algorithms deal with highly complex, ultra-high-dimensional problems, traditional particle learning strategies can only provide little help. In this paper, a particle swarm optimization algorithm with a hybrid variation domain dimension learning strategy is proposed, which uses the domain dimension average of the current particle dimension to generate guiding particles. At the same time, an improved inertia weight is also used, which effectively avoids the algorithm from easily falling into local optimum. To verify the strong competitiveness of the algorithm, the algorithm is tested on nineteen benchmark functions and compared with several well-known particle swarm algorithms. The experimental results show that the algorithm proposed in this paper has a significant effect on unimodal functions, and has a better effect on multimodal functions. Guided particles, improved inertia weight and mutation strategy can effectively balance local search and global search, and can better converge to the global optimal solution.
This paper proposes an elite coded particle swarm optimization (CEPSO) unmanned aerial vehicles (UAVs) search target motion for target detection in the shortest time. According to Bayesian theory, the problem of moving object searched is transformed into the optimization of cost function, which is used to describe the probability of moving objects being found. In order to better fit the problem, a coded elite particle swarm optimization algorithm is used to encode the search trajectory of the UAV as the movement path of the particle in the process of generation and evolution. This coding method uses the cognitive properties and social learning properties of particle swarms to generate better solutions. At the same time, in order to increase the diversity of particle swarms, a method of elite particle perturbation is used, which greatly improves the detection probability. In order to verify the superiority of the algorithm in this paper, a large number of simulation experiments are carried out on the existing methods. The experimental results show that compared with PSO, the detection probability of the proposed CEPSO is improved by 6.9%, and the time performance is improved by 8.5%. It also compares with other meta heuristic algorithms, including differential evolution (DE), artificial bee colony (ABC), harmony search (HS), biogeographic-based optimization (BBO), invasive weed optimization (IWO), teaching-learningbased Optimization (TLBO). The experimental results show that the algorithm proposed in this paper has better detection accuracy and less detection time.
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