Abstract
Aiming at the shortcoming of precocity and slow convergence in the application of traditional algorithms to solve the Weapon-Target Assignment (WTA) problem, this paper proposed an intuitionistic fuzzy genetic algorithm that combined with simulated annealing Meta-Lamarckian learning strategy and adaptive mutation to improve the efficiency and speed of solving WTA problem. Firstly, it considered the various constraint functions of WTA problem, in which make the threat of remaining targets minimum and the damage from attacks maximum, established the mathematical model. Next, it defined the membership and non-membership functions of object and constraint function, and built the intuitionistic fuzzy WTA model on the basis of the “min-max” operator. Then, this paper designed a strategy of Meta-Lamarckian learning for simulated annealing and adaptive mutation to enhance the capability of local search and the speed of upper convergence for the algorithm. Finally, this method is effective via the simulation and the analysis of comparison with GA, PSO.
Highlights
Weapon target assignment (Weapon-Target Assignment WTA) is a key problem for battlefield command and decision, is a NP complete problem, the size of the solution space with the number of defensive weapons and targets to increase the number of exponential growth
Genetic algorithm has the advantages of strong global optimization and fast convergence speed, which can search robustly the WTA problem, but it has a long iterative time and is easy to fall into the local best[7].Aiming at the shortcoming, many scholars embed local search as a kind of learning mechanism into the genetic algorithm to enhance the performance of the algorithm, which depend on the choice of initial point and need to traverse all of the individual, affect learning efficiency and even lead to fall into the local optimum
M weapon platforms, the threat coefficient of target j is Vj, the kill probability of weapon i to kill target j is= pij (i 1=,K, n; j 1,K, m ), and target survival probability is qij 1 pij, the platform i can launch W i weapons at most, and kill xij weapons. if xij = 1, the target j is assigned to the weapon i, else xij = 0 .The goal of solving WTA problem is to determine the allocation scheme of the weapon platform for the incoming target, which can make the threat of the remaining target minimum and the maximum damage value, so building model: å Õ min f1(x) =
Summary
Weapon target assignment (Weapon-Target Assignment WTA) is a key problem for battlefield command and decision, is a NP complete problem, the size of the solution space with the number of defensive weapons and targets to increase the number of exponential growth. The paper [2] combine the genetic algorithm and ant algorithm to solve the WTA, shorten the reaction time of weapon system, but increase the complexity; the paper [3] proposed a discrete differential evolution algorithm, widely search the solution space to obtain good convergence performance; the paper [4] presented a particle focusing distance adaptive particle swarm algorithm for solving various WTA problem; the paper [5] design a chromosome encoding scheme to meet the constraints, solving the problem into a combinatorial optimization problem without constraints of the form; the paper [6] use genetic algorithm to solve WTA, being fast convergence but can’t fundamentally solve the premature convergence of genetic algorithm. This paper presents an intuitionistic fuzzy genetic algorithm (IFGA) with learning capacity
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