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

Rubenstein-Montano, Bonnie; Malaga, Ross A.

doi:10.1109/tevc.2002.802874

Cited by 45 publications

(19 citation statements)

References 43 publications

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“…Therefore, u * would not be the solution of (10), and the equivalence is proved. Table I summarizes the procedure for computing, according to our strategy, a point on the Pareto-front, which could least displease the different TSOs.…”

Section: B Optimization Of the Normalized Problemmentioning

confidence: 98%

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A fair method for centralized optimization of multi-TSO power systems

Phulpin

Begović

Petit

et al. 2009

International Journal of Electrical Power & Energy Systems

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Abstract-This paper addresses the problem of centralized optimization of an interconnected power system partitioned into several regions controlled by different transmission system operators (TSOs). It is assumed that those utilities have agreed to transferring some of their competencies to a centralized control center, which is in charge of setting the control variables in the entire system to satisfy every utility's individual objective. This paper proposes an objective method for centralized optimization of such multi-TSO power systems, which relies on the assumption that each TSO has a real-valued optimization function focusing on its control area only. This method is illustrated on the IEEE 118 bus system partitioned into three TSOs. It is applied to the optimal reactive power dispatch problem, where the control variables are the voltage settings for generators and compensators. After showing that the method has some properties of fairness, namely freedom from envy, efficiency, accountability, and altruism, we emphasize its robustness with respect to certain biased behavior of the different TSOs.

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Section: B Optimization Of the Normalized Problemmentioning

confidence: 98%

“…Negotiation is usually advocated to reach a fair solution for multi-party resource allocation problems [10]. In the case of multi-TSO operation issues, as the optimization scheme should handle short-term operation, negotiation can not be considered a suitable solution.…”

Section: Introductionmentioning

confidence: 99%

A fair method for centralized optimization of multi-TSO power systems

Phulpin

Begović

Petit

et al. 2009

International Journal of Electrical Power & Energy Systems

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“…al. [8] treated multi-agent negotiation as a constrained multi-objective optimisation problem in which they used a GA to evolve offers in the agreement zone. The GA used a weighted sum approach to handle the multiple objectives of each negotiation participant.…”

Section: A Multi-agent Negotiationmentioning

confidence: 99%

Multi-agent multi-issue negotiations with incomplete information: A Genetic Algorithm based on discrete surrogate approach

Kattan

Ong

Galván

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-In this paper we present a negotiation agent based on Genetic Algorithm (GA) and Surrogate Modelling for a multi-player multi-issue negotiation model under incomplete information scenarios to solve a resource-allocation problem. We consider a multi-lateral negotiation protocol by which agents make offers sequentially in consecutive rounds until the deadline is reached. Agents' offers represent suggestions about how to divide the available resources among all agents participating in the negotiation. Each agent may "Accept" or "Reject" the offers made by its opponents through selecting the "Accept" or "Reject" option. The GA is used to explore the space of offers and surrogates used to model the behaviours of individual opponent agents for enhanced genetic evolution of offers that is agreeable upon all agents. The GA population comprises of solution individuals that are formulated as matrices where a specialised three different search operators that take the matrix representation into considerations are considered. Experimental studies of the proposed negotiation agent under different scenarios demonstrated that the negotiations by the agents completed in agreement before the deadline is reached, while at the same time, maximising profits.

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“…The literature that relates to this work includes the following: 1) negotiation agents adopting GA [20], [24]- [27] (Section V-A) and 2) negotiation agents adopting BL [19], [28], [29] (Section V-B). Space limitation precludes all these works from being introduced here, and this section only discusses some of the more closely related works (e.g., [19], [20], [24]- [27]).…”

Section: R Elated Workmentioning

confidence: 99%

BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

Sim

Guo

Shi

2009

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL)a n d genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: 1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and 2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: 1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, 2) higher utilities than agents that adopt BL to learn only RP, and 3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines.

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

Cited by 45 publications

References 43 publications

A fair method for centralized optimization of multi-TSO power systems

A fair method for centralized optimization of multi-TSO power systems

Multi-agent multi-issue negotiations with incomplete information: A Genetic Algorithm based on discrete surrogate approach

BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

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