A weighted sum genetic algorithm to support multiple-party multiple-objective negotiations

Negotiations are a special class of group decision-making problems that can be formulated as constrained optimization problems and are characterized by high degrees of conflict among the negotiation participants. A variety of negotiation support techniques have been used to help find solutions accep...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2002-08, Vol.6 (4), p.366-377
Main Authors: Rubenstein-Montano, B., Malaga, R.A.
Format: Article
Language:English
Subjects:
Acceptability
Applied sciences
Constraint optimization
Decision making
Evolutionary computation
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Genetic mutations
Genetics
History
Humans
Machine learning
Mathematical models
Negotiations
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Operators
Optimization methods
Searching
Studies
Terrorism
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Summary:Negotiations are a special class of group decision-making problems that can be formulated as constrained optimization problems and are characterized by high degrees of conflict among the negotiation participants. A variety of negotiation support techniques have been used to help find solutions acceptable to all parties in a negotiation. The paper presents an approach that employs a genetic algorithm (GA) for finding acceptable solutions for multiparty multiobjective negotiations. The GA approach is consistent with the complex nature of real-world negotiations and is therefore capable of addressing more realistic negotiation scenarios than previous techniques in the literature allow. In addition to the traditional genetic operators of reproduction, crossover, and mutation, the search is enhanced with a new operator called trade. The trade operator simulates concessions that might be made by parties during the negotiation process. GA performance with the trade operator is compared to a traditional GA, nonlinear programming, a hill-climber, and a random search. Experimental results show the GA with the trade operator performs better than these other more traditional approaches.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2002.802874