Multisurrogate-Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems

International Journal of Aerospace Engineering

Jiang

et al. 2023

In order to make the new air duct structure trajectory correction projectile have good dynamic correction control effect, the control strategy of the projectile’s correction mechanism is studied in this paper. A design method of trajectory correction control strategy based on particle swarm optimization-cuckoo search (PSO-CS) hybrid algorithm is proposed to obtain the optimal control parameters that can make the projectile flight stable and correct accurately. Firstly, the mathematical model of the air duct structure projectile is established. Secondly, the multiobjective optimization problem is analyzed. The projectile’s correction control strategy optimization model is established by taking the start control time, the number of corrections, the correction working time, and the interval time as the control variables. The optimization model innovatively considers the influence of the correction action on the flight stability of the projectile and the influence of the start control time on the correction range. Finally, the PSO-CS hybrid algorithm is used to design the calculation method of the optimization model and solve the optimal correction working parameters. The simulation results indicate that the control strategy optimization model can be solved by the proposed calculation method. Moreover, optimal correction working parameters of the correction mechanism in the current state can be obtained. Compared with the results of using single PSO algorithm and CS algorithm, the correction scheme calculated by PSO-CS hybrid optimization algorithm is better. This correction control scheme can effectively reduce the impact point deviation and make the projectile flight stable. At the same time, the circular error probable (CEP) of the projectile after correction is reduced from 42.3 m to 4.6 m while the impact point dispersion is lowered. The research results show that the design method of correction control strategy proposed in this paper is effective for trajectory correction of the new air duct structure projectile.

Section: Calculation Methods Of Control Strategy Model Based Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Parameter Optimization of Trajectory Correction Control Strategy for Air Duct Structure Projectile

Cui

International Journal of Aerospace Engineering

Jiang

et al. 2023

International Journal of Intelligent Systems

“…Ji et al [38,39] proposed a dual-surrogate-assisted and multisurrogate-assisted multitasking PSO, which can obtain multiple optimal solutions of expensive multimodal optimization problems at low computational cost. Ammar et al [40] designed two new multiobjective binary PSO algorithms, which efectively solved multi-item capacitated lotsizing problem.…”

Section: Introductionmentioning

confidence: 99%

Energy Dispatching Based on an Improved PSO-ACO Algorithm

Song

et al. 2023

In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO-ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO-ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high-quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO-ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO-ACO algorithm can plan a site energy dispatching route with shorter route, less time-consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.

“…Ji et al proposed a multisurrogate-assisted multitasking particle swarm optimization to solve the problem of expensive multimodal optimization. Ji et al introduced multitasking evolution into PSO, which makes the algorithm have faster convergence speed and better optimization ability in high-dimensional problems [14]. Song et al proposed variable-size cooperative coevolutionary particle swarm optimization to solve the problem of "dimensional disaster."…”

Section: Introductionmentioning

confidence: 99%

A Novel Compact Particle Swarm Optimization for Optimizing Coverage of 3D in Wireless Sensor Network

Chai

Wireless Communications and Mobile Computing

et al. 2022

The problem of 3D coverage in a wireless sensor network (WSN) has always been an urgent problem to be solved. A novel compact particle swarm optimization algorithm (ncPSO) to solve this problem is proposed in this paper. This algorithm uses a Pareto distribution to describe the position of particle swarms. The ncPSO reduces memory usage compared to traditional heuristics. The ncPSO using the Pareto distribution is less likely to fall into local optima than other compact algorithms using the Gaussian distribution. We also add Gaussian perturbation strategy to the algorithm to better avoid the algorithm falling into local optimum. Among the test functions of CEC2013, the ncPSO achieves remarkable optimization ability on most test functions. Finally, we apply ncPSO to the 3D coverage problem of sensors. Compared with other algorithms, the ncPSO achieves satisfactory results.