Efficient Hybrid-Game Strategies Coupled to Evolutionary Algorithms for Robust Multidisciplinary Design Optimization in Aerospace Engineering

Lee, D S; Gonzalez, Felipé; Périaux, J.; Srinivas, K.

doi:10.1109/tevc.2010.2043364

Cited by 40 publications

(21 citation statements)

References 13 publications

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“…Both HAPMOEA and Hybridised EA are coupled to the aerodynamic analysis tool. Details and validations of HAPMOEA and Hybrid-Game can be found in references [1,3,17].…”

Section: Optimisation Methodsmentioning

confidence: 99%

“…In addition, Lee et al [3] hybridised NSGA-II with Nash-Game strategy to study a role of Nash-Players in Hybrid-Game by solving multi-objective mathematical design problems convex, discontinuous, etc. And Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithm (HAPMOEA) [4] is also hybridised to solve a real-world robust multidisciplinary design problem.…”

Section: Introductionmentioning

confidence: 99%

“…Numerical results show that the Hybrid-Game improves 70% of HAPMOEA performance while producing better Pareto optimal solutions. References [1,3,5] clearly describes merits of using Hybrid-Game coupled to MOEA for engineering design applications which consider a complex geometry or a large number of design variables. Hybrid-Game has two major characteristics; the first is a decomposition of design problem, for instance, a multi-objective design problem will be split into several simpler single-objective problems corresponding to Nash-Players which have their own design search space.…”

Section: Introductionmentioning

confidence: 99%

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Double Shock Control Bump design optimization using hybridised evolutionary algorithms

Lee

Périaux

Pons‐Prats

et al. 2010

IEEE Congress on Evolutionary Computation

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Abstract-The paper investigates two advanced optimisation methods for solving active flow control device shape design problem and also compares their optimisation efficiency in terms of computational cost and design quality. The first optimisation method uses Hierarchical Asynchronous Parallel Multi-Objective Evolutionary Algorithm (HAPMOEA) and the second uses Hybridized EA with Nash-Game strategies. Both optimisation method are based on a canonical evolution strategy and incorporates the concepts of parallel computing and asynchronous evaluation. For the practical test case, one of active flow control devices named Shock Control Bump (SCB) is considered and it is applied to Natural Laminar Flow (NLF) aerofoil. The concept of SCB is to decelerate supersonic flow on upper/lower surface of transonic aerofoil that leads delay of shock occurrence. Such active flow technique reduces a total drag at transonic speeds.Numerical results clearly show that Hybrid-Game helps EA to accelerate optimisation process, and also applying SCB on the suction and pressure sides significantly reduces transonic wave drag and improves lift on drag (L/D) value when compared to the baseline design.

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“…Both HAPMOEA and Hybridised EA are coupled to the aerodynamic analysis tool. Details and validations of HAPMOEA and Hybrid-Game can be found in references [1,3,17].…”

Section: Optimisation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Double Shock Control Bump design optimization using hybridised evolutionary algorithms

Lee

Périaux

Pons‐Prats

et al. 2010

IEEE Congress on Evolutionary Computation

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“…Call P the problem formed by all P i s. Parallel genetic algorithm [25] developed in [24] and the idea of co-evolution [26] are implemented to solve P. The idea is extended such that each (sub-)problem P i has its own objective function. This concept is used in [27] to gain faster convergence to Pareto solution in multiobjective optimisation problem. Let x −i be a vector containing the decision variables of all agents involved in problem P i excluding that of the agent i.…”

Section: Parallel Search Algorithmmentioning

confidence: 99%

Agent Based Modelling for Water Resource Allocation in the Transboundary Nile River

Ding

Erfani

Mokhtar

et al. 2016

Water

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Abstract:Water resource allocation is the process of assessing and determining a mechanism on how water should be distributed among different regions, sectors and users. Over the recent decades, the optimal solution for water resource allocation has been explored both in centralised and decentralised mechanisms. Conventional approaches are under central planner suggesting a solution which maximises total welfare to the users. Moving towards the decentralised modelling, the techniques consider individuals as if they act selfishly in their own favour. While central planner provides an efficient solution, it may not be acceptable for some selfish agents. The contrary is true as well in decentralised solution, where the solution lacks efficiency leading to an inefficient usage of provided resources. This paper develops a parallel evolutionary search algorithm to introduce a mechanism in re-distributing the central planner revenue value among the competing agents based on their contribution to the central solution. The result maintains the efficiency and is used as an incentive for calculating a fair revenue for each agent. The framework is demonstrated and discussed to allocate water resources along the Nile river basin, where there exist eleven competing users represented as agents in various sectors with upstream-downstream relationships and different water demands and availability.

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“…Since Nash 1,2 introduces Nash Equilibrium (NE) theory in the early 50's, it has become an efficient tool to solve Multi Objective Optimization (MOO) problems in aerodynamics 3,4,5,6 and other relative fields 7,8 . The solution of a MOO problem can be viewed as a NE under the concept of competitive games.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Aerodynamic Optimization Using Parallel Nash Evolutionary/Deterministic Hybrid Algorithms

Tang

2016

Int. J. Mod. Phys. Conf. Ser.

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This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.

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Efficient Hybrid-Game Strategies Coupled to Evolutionary Algorithms for Robust Multidisciplinary Design Optimization in Aerospace Engineering

Cited by 40 publications

References 13 publications

Double Shock Control Bump design optimization using hybridised evolutionary algorithms

Double Shock Control Bump design optimization using hybridised evolutionary algorithms

Agent Based Modelling for Water Resource Allocation in the Transboundary Nile River

Multi Objective Aerodynamic Optimization Using Parallel Nash Evolutionary/Deterministic Hybrid Algorithms

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