Improved Genetic Algorithm for Weapon Target Assignment Problem

2021 ◽  
Author(s):  
Luo Ruining ◽  
Zhao Yan
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Improved Genetic Algorithm ◽  
Target Assignment

Weapon-Target Assignment Problem in the Warship Fleet Based on Fast and Elitist Non-Dominated Sorting Genetic Algorithm

2012 ◽  
Vol 605-607 ◽  
pp. 2399-2404
Author(s):  
Xin Lai Chen ◽  
Song Shen
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Pareto Solution ◽  
Robust Solution ◽  
Target Assignment

Represent a new Weapon-Target Assignment (WTA) model of warship fleet as to the characteristic of the modern naval battle field and the battle modality. This model considers the WTA to a multi-objects optimization problem, and a Fast and Elitist Non-Dominated Sorting Genetic Algorithm (FENSGA) is applied to resolve this model. The FENSGA can reach a set of wide-distributing, robust solution. One running of the FENSGA can reach a multi-Pareto solution, which the commander can select from. A simulation is given to prove the validity of this model and algorithm.

A New Genetic Algorithm for Continuous Berth and Quay Crane Allocation Problem

2011 ◽  
Vol 97-98 ◽  
pp. 619-622 ◽  
Author(s):  
Na Li ◽  
Zhi Hong Jin ◽  
Erick Massami
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Container Terminal ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Improved Genetic Algorithm ◽  
Berth Allocation Problem ◽  
The Real ◽  
Quay Cranes ◽  
Quay Crane Assignment

The combined optimization of continuous berth allocation problem and quay crane assignment problem are solved. Considering the real constraints of container terminal, an improved genetic algorithm is proposed. The chromosome is composed of berthing time, berthing location and number of quay cranes. While in the following, specific quay cranes are fixed to assign to ships. Through comparisons with the former two literatures, the results are improved averagely by 33.78% and 28.57% respectively by the proposed genetic algorithm, which shows its effectiveness.

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