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

Bonnie Rubenstein-Montano, Ross Malaga

Research output: Contribution to journalArticleResearchpeer-review

40 Citations (Scopus)

Abstract

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. This 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.

Original languageEnglish
Pages (from-to)366-377
Number of pages12
JournalIEEE Transactions on Evolutionary Computation
Volume6
Issue number4
DOIs
StatePublished - 1 Aug 2002

Fingerprint

Multiple Objectives
Weighted Sums
Genetic algorithms
Genetic Algorithm
Mathematical operators
Operator
Constrained optimization
Nonlinear programming
Decision making
Random Search
Genetic Operators
Group Decision Making
Constrained Optimization Problem
Nonlinear Programming
Crossover
Mutation
Scenarios
Trade
Experimental Results

Keywords

  • Constrained optimization
  • Genetic algorithms
  • Multicriterion optimization
  • Negotiations

Cite this

@article{c9c9107fbbfa4a6b9b62a35c645e0108,
title = "A weighted sum genetic algorithm to support multiple-party multiple-objective negotiations",
abstract = "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. This 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.",
keywords = "Constrained optimization, Genetic algorithms, Multicriterion optimization, Negotiations",
author = "Bonnie Rubenstein-Montano and Ross Malaga",
year = "2002",
month = "8",
day = "1",
doi = "10.1109/TEVC.2002.802874",
language = "English",
volume = "6",
pages = "366--377",
journal = "IEEE Transactions on Evolutionary Computation",
issn = "1089-778X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

A weighted sum genetic algorithm to support multiple-party multiple-objective negotiations. / Rubenstein-Montano, Bonnie; Malaga, Ross.

In: IEEE Transactions on Evolutionary Computation, Vol. 6, No. 4, 01.08.2002, p. 366-377.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A weighted sum genetic algorithm to support multiple-party multiple-objective negotiations

AU - Rubenstein-Montano, Bonnie

AU - Malaga, Ross

PY - 2002/8/1

Y1 - 2002/8/1

N2 - 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. This 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.

AB - 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. This 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.

KW - Constrained optimization

KW - Genetic algorithms

KW - Multicriterion optimization

KW - Negotiations

UR - http://www.scopus.com/inward/record.url?scp=0036672609&partnerID=8YFLogxK

U2 - 10.1109/TEVC.2002.802874

DO - 10.1109/TEVC.2002.802874

M3 - Article

VL - 6

SP - 366

EP - 377

JO - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

SN - 1089-778X

IS - 4

ER -