The Experts below are selected from a list of 46149 Experts worldwide ranked by ideXlab platform
Guixu Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Pareto Optimal set approximation by models a linear case
International Conference on Evolutionary Multi-criterion Optimization, 2019Co-Authors: Aimin Zhou, Haoying Zhao, Hu Zhang, Guixu ZhangAbstract:The optimum of a multiobjective optimization problem (MOP) usually consists of a set of tradeoff solutions, called Pareto Optimal set, that balances different objectives. In the community of evolutionary computation, an internal or external population with a limited size is usually used to approximate the Pareto Optimal set. Since the Pareto Optimal set forms a manifold in both the decision and objective spaces under mild conditions, it is possible to use a model as well as a population of solutions to approximate the Pareto Optimal set. Following this idea, the paper proposes to use a set of linear models to approximate the Pareto Optimal set in the decision space. The basic idea is to partition the manifold into different segments and use a linear model to approximate each segment in a local area. To implement the algorithm, the models are incorporated in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework. The proposed algorithm is applied to a test suite, and the comparison study demonstrates that models can help to improve the performance of algorithms that only use solutions to approximate the Pareto Optimal set.
Chi Xie - One of the best experts on this subject based on the ideXlab platform.
-
The Pareto-Optimal Solution Set of the Equilibrium Network Design Problem with Multiple Commensurate Objectives
Networks and Spatial Economics, 2011Co-Authors: Dung-ying Lin, Chi XieAbstract:The focus of this paper is to develop a solution framework to study equilibrium transportation network design problems with multiple objectives that are mutually commensurate. Objective parameterization, or scalarization, forms the core idea of this solution approach, by which a multi-objective problem can be equivalently addressed by tackling a series of single-objective problems. In particular, we develop a parameterization-based heuristic that resembles an iterative divide-and-conquer strategy to locate a Pareto-Optimal solution in each divided range of commensurate parameters. Unlike its previous counterparts, the heuristic is capable of asymptotically exhausting the complete Pareto-Optimal solution set and identifying parameter ranges that exclude any Pareto-Optimal solution. Its algorithmic effectiveness and solution characteristics are justified by a set of numerical examples, from which we also gain additional insights about its solution generation behavior and the tradeoff between the computation cost and solution quality.
Kozo Fujii - One of the best experts on this subject based on the ideXlab platform.
-
1 Data Mining of Pareto-Optimal Transonic Airfoil Shapes Using Proper Orthogonal Decomposition
2016Co-Authors: Akira Oyama, Taku Nonomura, Kozo FujiiAbstract:A new approach to extract useful design information from Pareto-Optimal solutions of optimization problems is proposed and applied to an aerodynamic transonic airfoil shape optimization. The proposed approach enables an analysis of line, face, or volume data of all Pareto-Optimal solutions such as shape and flow field by decomposing the data into principal modes and corresponding base vectors using proper orthogonal decomposition (POD). Analysis of the shape and surface pressure data of the Pareto-Optimal solutions of an aerodynamic transonic airfoil shape optimization problem showed that the optimized airfoils can be categorized into two families (low drag designs and high lift designs), where the lift is increased by changing the camber near the trailing edge among the low drag designs while the lift is increased by moving the lower surface upward among the high lift designs. Nomenclature am(n) = eigenvector of mode m c = chord length Cd = drag coefficient Cl = lift coefficien
-
data mining of Pareto Optimal transonic airfoil shapes using proper orthogonal decomposition
Journal of Aircraft, 2010Co-Authors: Akira Oyama, Taku Nonomura, Kozo FujiiAbstract:A new approach to extract useful design information from Pareto-Optimal solutions of optimization problems is proposed and applied to an aerodynamic transonic airfoil shape optimization. The proposed approach enables an analysis of line, face, or volume data of all Pareto-Optimal solutions such as shape and flow field by decomposing the data into principal modes and corresponding base vectors using proper orthogonal decomposition (POD). Analysis of the shape and surface pressure data of the Pareto-Optimal solutions of an aerodynamic transonic airfoil shape optimization problem showed that the optimized airfoils can be categorized into two families (low drag designs and high lift designs), where the lift is increased by changing the camber near the trailing edge among the low drag designs while the lift is increased by moving the lower surface upward among the high lift designs.
Lajos Hanzo - One of the best experts on this subject based on the ideXlab platform.
-
a quantum search aided dynamic programming framework for Pareto Optimal routing in wireless multihop networks
IEEE Transactions on Communications, 2018Co-Authors: Dimitrios Alanis, Panagiotis Botsinis, Zunaira Babar, Hung Viet Nguyen, Daryus Chandra, Lajos HanzoAbstract:Wireless multihop networks (WMHNs) have to strike a trade-off among diverse and often conflicting quality-of-service requirements. The resultant solutions may be included by the Pareto front under the concept of Pareto Optimality. However, the problem of finding all the Pareto-Optimal routes in WMHNs is classified as non-deterministic polynomial-hard, since the number of legitimate routes increases exponentially, as the nodes proliferate. Quantum computing offers an attractive framework of rendering the Pareto-Optimal routing problem tractable. In this context, a pair of quantum-assisted algorithms has been proposed, namely the non-dominated quantum optimization and the non-dominated quantum iterative optimization. However, their complexity is proportional to $\sqrt {N}$ , where $N$ corresponds to the total number of legitimate routes, thus still failing to find the solutions in “polynomial time.” As a remedy, we devise a dynamic programming framework and propose the so-called evolutionary quantum Pareto optimization (EQPO) algorithm. We analytically characterize the complexity imposed by the EQPO algorithm and demonstrate that it succeeds in solving the Pareto-Optimal routing problem in polynomial time. Finally, we demonstrate by simulations that the EQPO algorithm achieves a complexity reduction, which is at least an order of magnitude when compared to its predecessors, albeit at the cost of a modest heuristic accuracy reduction.
Aimin Zhou - One of the best experts on this subject based on the ideXlab platform.
-
Pareto Optimal set approximation by models a linear case
International Conference on Evolutionary Multi-criterion Optimization, 2019Co-Authors: Aimin Zhou, Haoying Zhao, Hu Zhang, Guixu ZhangAbstract:The optimum of a multiobjective optimization problem (MOP) usually consists of a set of tradeoff solutions, called Pareto Optimal set, that balances different objectives. In the community of evolutionary computation, an internal or external population with a limited size is usually used to approximate the Pareto Optimal set. Since the Pareto Optimal set forms a manifold in both the decision and objective spaces under mild conditions, it is possible to use a model as well as a population of solutions to approximate the Pareto Optimal set. Following this idea, the paper proposes to use a set of linear models to approximate the Pareto Optimal set in the decision space. The basic idea is to partition the manifold into different segments and use a linear model to approximate each segment in a local area. To implement the algorithm, the models are incorporated in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework. The proposed algorithm is applied to a test suite, and the comparison study demonstrates that models can help to improve the performance of algorithms that only use solutions to approximate the Pareto Optimal set.
