Research on Multiaircraft Cooperative Suppression Interference Array Based on an Improved Multiobjective Particle Swarm Optimization Algorithm

Abstract

For the problem of multiaircraft cooperative suppression interference array (MACSIA) against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.

Dynamic Multiaircraft Cooperative Suppression Interference Array Optimization by Dynamic MOPSO Algorithm

“…Finally, determine whether the minimum width of the route planning safety zone satisfying the given width limit is formed and the minimum width is calculated. On this part of the content which can also be seen in our previous research results [6,7], the specific calculating steps are shown in Figure 1. The calculation model of the range of enemy air defense radar under terrain shading and electronic jamming in the process is as follows [6,7].…”

“…In a work by Zhang et al [6], a multiobjective optimization model of MACSIA was constructed under static conditions. A modified multiobjective particle swarm optimization algorithm is used to solve the static MACSIA 2 Mathematical Problems in Engineering optimization problem [7]. Literatures 6 and 7 are our previous research results, but only the MACSIA optimization problem under static conditions is studied.…”

Unmanned Aerial Vehicle Swarm Cooperative Decision-Making for SEAD Mission: A Hierarchical Multiagent Reinforcement Learning Approach

“…Researchers on UAV swarm cooperative decision-making have paid their great efforts in rule-based methods [7,8], influence diagram methods [9,10], computational intelligence methods [11], search-based intelligent optimization algorithms [3,12,13], and learning-based reinforcement learning (RL) algorithms [14][15][16][17][18][19].…”

“…The search-based intelligent optimization algorithms can find the optimal or suboptimal solution of the explicit objective function through the parallel iteration optimization mechanism. Zhang et al [12] studied the UAV path planning problem in electronic warfare. Multi-objective particle swarm optimization algorithm was used to solve the optimal path and jamming array mode of the jammer.…”

Cooperative Route Planning for Multiple Aircraft in a Semifree ATC System

“…Swarm Intelligence Algorithms. Many swarm intelligence algorithms have been developed for solving the route planning problem, including the genetic algorithm (GA) [10], evolutionary programming (EP) [11,12], the Particle Swarm Optimization Algorithm (PSO) [13], and ant colony optimization (AOC) [14,15]. These algorithms are highly robust and flexible and can be used to solve different types of optimization problems; however, the computation speed limits its application in route planning for multiple aircraft.…”