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Evolutionary decision-makings for the dynamic weapon-target assignment problem

Science in China(Series F) 2009年第11期52卷 2006-2018页

作者： CHEN Jie1,2, XIN Bin1,2, PENG ZhiHong1,2, DOU LiHua1,2 & ZHANG Juan1,2 1 School of Automation, Beijing Institute of Technology, Beijing 100081, China 2 Key Laboratory of Complex System Intelligent Control and Decision, Ministry of Education, Beijing 100081, China School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.

关键词： decision-making dynamic weapon-target assignment (DWTA) military command and control evolutionary computation memetic algorithms constraints handling

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LDSE Algorithm for Constrained Multi-Objective Optimization with Mixed Variables

LDSE Algorithm for Constrained Multi-Objective Optimization ...

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The 4th Across-strait Workshop on Shock Wave/Vortex Interaction

作者： Changtong Luo~*,Shao-Liang Zhang~+ Department of Computational Science and Engineering Nagoya University, Nagoya 464-8603,Japan

A bi-level tournament selection method for handling multi-objectives and constraints is provided for low dimensional simplex evolution(LDSE).The idea is general and can apply to other evo utionary algorithms.

关键词： multi-objective optimization evolutionary algorithm genetic algorithm constraints handling mixed-integer programming

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