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Using genetic algorithms in effects-based planning
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
Swedish Defense Research Agency (FOI).
Swedish Defense Research Agency (FOI).
2013 (English)In: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE Computer Society, 2013, 438-443 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a genetic algorithm-based method for evaluation of operational plans within effects-based planning. We formulate the effects-based planning problem as a bi-objective optimization problem, in which the distance from the initial state to the current state (g) and the distance from the current state to the desired end state (h) are minimized. To solve the problem, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Considering an expeditionary operation scenario, we simulate a subset of possible plans and present the decision maker with a set of promising plans which are capable of approaching the desired end state efficiently. In order to discuss the efficiency and effectiveness of the algorithm, we compare the results of NSGA-II with the results of A*. The computational results show that NSGA-II is much more efficient than A* with regard to g. On the other hand A* is a little more effective with regard to h.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 438-443 p.
Series
IEEE International Conference on Systems Man and Cybernetics Conference Proceedings, ISSN 1062-922X
Keyword [en]
Effects-based planning, Genetic algorithms, Optimization, Path planning, Search algorithms
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-139367DOI: 10.1109/SMC.2013.80ISI: 000332201900074Scopus ID: 2-s2.0-84893539170ISBN: 978-1-4799-0652-9 (print)OAI: oai:DiVA.org:kth-139367DiVA: diva2:685992
Conference
The 2013 IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013 Manchester, UK
Note

QC 20140128

Available from: 2014-01-10 Created: 2014-01-10 Last updated: 2014-04-24Bibliographically approved
In thesis
1. Using Genetic Algorithms for Large Scale Optimizationof Assignment, Planning and Rescheduling Problems
Open this publication in new window or tab >>Using Genetic Algorithms for Large Scale Optimizationof Assignment, Planning and Rescheduling Problems
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There has always been a need to solve real-life large-scale problems, suchas efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimization genre. A class of combinato- rial optimization problems is NP-hard and, among many well-known, several of them are assignment, planning and rescheduling problems. Assignment problems can deal with optimal assignment of teams of collaborating agents; planning problems can be effects-based planning that search for promising plans to get desired end states with minimal cost; rescheduling problems can be multi-criteria optimization of rescheduling resources that modify existing original schedule. These large scale optimization problems are complex with intractable and highly complex search spaces. Currently, there are no known algorithms with polynomial time complexity, which can solve these problems. Genetic Algorithms have been successfully applied to solve many complex optimization problems but not to the specific problems mentioned above.

The aim of the research, presented in this thesis, is to use Genetic Algo- rithms for large scale optimization of assignment, planning and rescheduling problems. More specifically, the contributions of the thesis are to: (i) adapt existing and develop new efficient Genetic Algorithms to solve large scale as- signment problems, and (ii) adapt existing Genetic Algorithms to solve large scale effects-based planning, and multi-objective rescheduling optimization problems.In case of assignment, we solve a team assignment problem and investigate specific regions in a solution space for assignment problems with huge search spaces.

For the team assignment, an existing Genetic Algorithm is adapted and applied for optimal assignment of tasks to teams of collaborating agents. The algorithm is scalable, stable, robust and produces a near optimal solution. The results of the team assignment problem show that the existing Genetic Algorithms are not efficient for optimal assignment of tasks to teams of agents. Hence, to solve larger instances of the problem efficiently, new Genetic Algo- rithms are developed with emphasis on the construction of crossover opera- tors. Since teams assignment can be multi-criteria, a multi-objective model is constructed and two widely used multi-objective evolutionary algorithms are applied. Further, for the assignment problems with huge search spaces, an existing Genetic Algorithm is adapted to extract possible combinations of input parameters from a specified solution space region. To solve the large scale effects-based planning, a multi-objective optimization problem is formu- lated for the evaluation of operational plans and a multi-objective Genetic Algorithm is adapted and applied to the problem. The results show that the suggested algorithm is much more efficient than A*. For the rescheduling problem, a multi-objective optimization model for rescheduling of resources is proposed and a multi-objective Genetic Algorithm is adapted and applied to obtain the Pareto-optimal solutions.

The research presented in this thesis confirms that Genetic Algorithms can be used for large scale assignment, planning and rescheduling problems since they have shown to be suitable in solving these problems efficiently.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2014. 202 p.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 14:06
National Category
Computer Systems
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-143671 (URN)978-91-7595-047-1 (ISBN)
Public defence
2014-04-25, Sal D, Forum, Isafjordsgatan 39, Kista, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20140328

Available from: 2014-03-28 Created: 2014-03-27 Last updated: 2014-03-28Bibliographically approved

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