A discrete particle swarm optimization algorithm applied in constrained static weapon-target assignment problem

The weapon-target assignment (WTA) problem is an important content in military operational research. In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the constrained static weapon-target assignment (SWTA) problem subject to firing range constraint, weapon-...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2016 12th World Congress on Intelligent Control and Automation (WCICA) s. 3118 - 3123
Hlavní autori: Yili Zhou, Xiaobo Li, Yifan Zhu, Weiping Wang
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2016
Predmet:
Encoding
Genetic algorithms
Mathematical model
Optimization
Particle swarm optimization
Weapons
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The weapon-target assignment (WTA) problem is an important content in military operational research. In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the constrained static weapon-target assignment (SWTA) problem subject to firing range constraint, weapon-target match constraint and so on. This constrained SWTA problem with upper bound on the number of weapons available to each weapon platform is a NP-complete mathematical problem to assign weapon platforms to targets properly. Unlike those one-to-one WTA problems, in this constrained SWTA model, one weapon platform can be assigned to several targets and one target can be attacked by more than one weapon in saturation attack. A special encoding strategy is used to satisfy the firing range constraint, weapon-target match constraint and available weapon quantity constraint. The proposed algorithm introduces the uniform mutation and crossover concepts of genetic algorithm (GA) into standard PSO algorithm to generate the update equation of the proposed DPSO algorithm. And penalty function method is adopted to deal with other constraints by adding restrictions to objective function as the fitness function. The simulation results demonstrate the proposed DPSO algorithm is very efficient to solve this constrained SWTA problem and superior to general GA and standard PSO algorithm.
DOI:10.1109/WCICA.2016.7578704