Multi-objective optimization of engineering systems using game theory and particle swarm optimization

Annamdas, Kiran K.; Rao, Singiresu S.

doi:10.1080/03052150902822141

Cited by 49 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The contribution of this paper to the literature is twofold: (i) proposing a novel bi-objective mathematical model by utilizing a reliability approach (Snyder and Daskin, 2005) for designing a reliable network of facilities in healthcare service under uncertainties, and (ii) presenting a new hybrid solution approach by combining interval programming, stochastic programming and fuzzy programming given by (Wang et al, 2012), queuing theory by (Wang, 2004) and game theory by Annamdas and Rao (2009). The proposed hybrid method can handle mixed uncertainties expressed as intervals as well as possibilistic and probabilistic distributions.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we applied modified game theory (MGT) approach proposed by Annamdas and Rao (2009), for solving the proposed model. In this approach, all the objective functions/players are supposed to concur to find a compromise solution according to a mutually agreeable bargaining model or super-criterion.…”

Section: Negotiation Based Solving Approachmentioning

confidence: 99%

“…Beliën et al (2013) developed a mathematical programming model to determine the optimal location of organ transplant centers, in order to minimize the sum of the weighted time components from the moment a donor organ becomes available till organ implantation. The contribution of this paper to the literature of the study is twofold: (i) proposing a novel multi-objective mathematical model by utilizing a reliability approach (Snyder and Daskin, 2005) for designing a reliable network of facilities in healthcare service under uncertainties, and (ii) presenting a new hybrid solution approach by combining interval programming, stochastic programming and fuzzy programming by (Wang et al, 2012), queuing theory by (Wang, 2004) and game theory by Annamdas and Rao (2009).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

Mohammadi¹,

Dehbari

Vahdani

2014

Transportation Research Part E: Logistics and Transportation Re

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Negotiation Based Solving Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

Mohammadi¹,

Dehbari

Vahdani

2014

Transportation Research Part E: Logistics and Transportation Re

View full text Add to dashboard Cite

“…About the game pattern, the pure competitive pattern exists [6][7][8][13][14][15][16]; namely, all players adopt competitive behavior to obtain benefit. Pure cooperative pattern also exists [8,[16][17][18]; namely, all players adopt the same cooperative behavior to obtain benefit.…”

Section: Instructionmentioning

confidence: 99%

Multiobjective Game Method Based on Self-Adaptive Space Division of Design Variables and Its Application to Vehicle Suspension

Meng

Xie

Wang

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Based on the similarity between the game theory and the multiobjective design, the bionic mapping and the space mapping are established between the multiobjective optimization model and game model. Then, the multiobjective optimization method based on self-adaptive space division of design variables is proposed. The design variables are divided into multiple strategy subspaces and are assigned to corresponding game players by calculating impact factors,K-means clustering, and correlation analysis. Strategy subspaces of game players are dynamically adjusted in the iteration process. In their own strategy subspaces, each game player takes their payoff function (the mapping of objective function) as monoobjective optimization. It gives the best strategy upon other players. And the best strategies of all players are combined into the group strategy in this game round. Triobjective optimization is carried out for vehicle suspension in this method and it is compared with the traditional game method. The results show that this method has better calculating automaticity and can effectively promote generalization of multiobjective game method and improve the computational efficiency and precision.

show abstract

“…The selection of players can vary from actual persons, agents to aircraft components evaluated when different disciplines are involved, Runyan et al [161], considering different disciplines/technologies as players, Habbal et al [162], and based on gene expression programming in multi-objective MDO problems, Xiao et al [163]. Players could also be fictitious, each having control 41 of one design variable in a particle swarm optimisation, Annamdas et al [164], or even objective functions in a multi-objective optimal engineering design, Gonzalez et al [165] and Hu et al [166]. Finally, a hybrid-game strategy for multi-objective design optimization was proposed by Lee et al [167], employing Nash equilibrium as a fast companion optimizer to guide the slower multiobjective evolutionary optimizer, capturing the Pareto non-dominated front.…”

Section: Game Theory In Engineering Designmentioning

confidence: 99%

Development and application of a value driven design assessment framework to an unmanned air system design

Papageorgiou

2018

2018 Advances in Science and Engineering Technology International Conferences (ASET)

View full text Add to dashboard Cite

The work presented in this thesis concerns the development of a value driven engineering design assessment framework and its application to the conceptual design of an Unmanned Air System (UAS) to be utilised in a defence application. This research demonstrates the implementation of the value driven design philosophy in this framework, identifying value enhancing designs, with value not converted to monetary worth and as perceived by all stakeholders involved. A multi-criteria and multi-stakeholder decision making analysis is adopted to address their preferences as well as to study their interacting strategic choices. The ultimate objective of this framework is to convert engineering design to a decision making analysis with multiple conflicting objectives of multiple stakeholders considered.This framework is capable of providing a product definition and estimation of all performance and cost related attributes for the conceptual phase. However, instead of pertaining to a single aircraft concept, a broad range of combinations of UAS configurations and geometries is generated by systematically searching alternative concepts and design configurations through a novel parameterization of the aircraft geometric topologies.Value, related to the designed system's capabilities or performance and lifecycle cost, is used to compare different alternatives in the decision making of engineering design through the appropriate value model. Following a valuefocused approach, a novel multi-attribute value model is introduced for objectively capturing the stakeholder's preferences and expectations. Furthermore, a more sophisticated multi-attribute utility model, based on standard Multi-attribute Utility Theory, is employed in the evaluation.Game Theory as an optimization tool is used to develop a novel hybrid cooperative/non-cooperative non-zero sum, complete information game among all involved stakeholders as players. This game successfully addresses the stakeholders' preferences in a functional outcome-focused way, resolving the high indeterminacy of the alternative designs through a cooperative game. At the same time, their strategic interactions are captured in a process-focused non-cooperative game. Hence, the optimal design is identified through the simultaneous employment of the Nash bargaining solution and the Nash equilibrium.i

show abstract

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

Cited by 49 publications

References 15 publications

Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

Multiobjective Game Method Based on Self-Adaptive Space Division of Design Variables and Its Application to Vehicle Suspension

Development and application of a value driven design assessment framework to an unmanned air system design

Contact Info

Product

Resources

About