The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Alan W. Whisman - One of the best experts on this subject based on the ideXlab platform.
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a combined Tchebycheff aspiration criterion vector interactive multiobjective programming procedure
Management Science, 1993Co-Authors: Ralph E. Steuer, Joe Silverman, Alan W. WhismanAbstract:In this paper we combine the Tchebycheff method of Steuer and Choo with Wierzbicki's Aspiration Criterion Vector method in order to form an improved procedure for interactive multiple objective programming. The Combined procedure is sensible because the Tchebycheff and Aspiration Criterion Vector methods possess complementary distinguishing characteristics, solve similar optimization problems to probe the nondominated set, and share a similar computer/user interface. In the early iterations, Tchebycheff probes of the nondominated set might be conducted to locate promising neighborhoods of search. In later iterations, Aspiration Criterion Vector probes might be used to pinpoint a final solution. Computational experience is reported showing the improved effectiveness of the Combined Procedure when employed in this fashion when compared against the Tchebycheff and Aspiration Criterion Vector methods run separately.
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A combined Tchebycheff/aspiration criterion vector interactive multiobjective programming procedure
Management Science, 1993Co-Authors: Ralph E. Steuer, Joe Silverman, Alan W. WhismanAbstract:In this paper we combine the Tchebycheff method of Steuer and Choo with Wierzbicki's Aspiration Criterion Vector method in order to form an improved procedure for interactive multiple objective programming. The Combined procedure is sensible because the Tchebycheff and Aspiration Criterion Vector methods possess complementary distinguishing characteristics, solve similar optimization problems to probe the nondominated set, and share a similar computer/user interface. In the early iterations, Tchebycheff probes of the nondominated set might be conducted to locate promising neighborhoods of search. In later iterations, Aspiration Criterion Vector probes might be used to pinpoint a final solution. Computational experience is reported showing the improved effectiveness of the Combined Procedure when employed in this fashion when compared against the Tchebycheff and Aspiration Criterion Vector methods run separately.
Ralph E. Steuer - One of the best experts on this subject based on the ideXlab platform.
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Interactive multiple objective programming using Tchebycheff programs and artificial neural networks
Computers & Operations Research, 2000Co-Authors: Minghe Sun, Antonie Stam, Ralph E. SteuerAbstract:A new interactive multiple objective programming procedure is developed that combines the strengths of the interactive weighted Tchebycheff procedure (Steuer and Choo. Mathematical Programming 1983;26(1):326–44.) and the interactive FFANN procedure (Sun, Stam and Steuer. Management Science 1996;42(6):835–49.). In this new procedure, nondominated solutions are generated by solving augmented weighted Tchebycheff programs (Steuer. Multiple criteria optimization: theory, computation and application. New York: Wiley, 1986.). The decision maker indicates preference information by assigning “values” to or by making pairwise comparisons among these solutions. The revealed preference information is then used to train a feed-forward artificial neural network. The trained feed-forward artificial neural network is used to screen new solutions for presentation to the decision maker on the next iteration. The computational experiments, comparing the current procedure with the interactive weighted Tchebycheff procedure and the interactive FFANN procedure, produced encouraging results
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a combined Tchebycheff aspiration criterion vector interactive multiobjective programming procedure
Management Science, 1993Co-Authors: Ralph E. Steuer, Joe Silverman, Alan W. WhismanAbstract:In this paper we combine the Tchebycheff method of Steuer and Choo with Wierzbicki's Aspiration Criterion Vector method in order to form an improved procedure for interactive multiple objective programming. The Combined procedure is sensible because the Tchebycheff and Aspiration Criterion Vector methods possess complementary distinguishing characteristics, solve similar optimization problems to probe the nondominated set, and share a similar computer/user interface. In the early iterations, Tchebycheff probes of the nondominated set might be conducted to locate promising neighborhoods of search. In later iterations, Aspiration Criterion Vector probes might be used to pinpoint a final solution. Computational experience is reported showing the improved effectiveness of the Combined Procedure when employed in this fashion when compared against the Tchebycheff and Aspiration Criterion Vector methods run separately.
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A combined Tchebycheff/aspiration criterion vector interactive multiobjective programming procedure
Management Science, 1993Co-Authors: Ralph E. Steuer, Joe Silverman, Alan W. WhismanAbstract:In this paper we combine the Tchebycheff method of Steuer and Choo with Wierzbicki's Aspiration Criterion Vector method in order to form an improved procedure for interactive multiple objective programming. The Combined procedure is sensible because the Tchebycheff and Aspiration Criterion Vector methods possess complementary distinguishing characteristics, solve similar optimization problems to probe the nondominated set, and share a similar computer/user interface. In the early iterations, Tchebycheff probes of the nondominated set might be conducted to locate promising neighborhoods of search. In later iterations, Aspiration Criterion Vector probes might be used to pinpoint a final solution. Computational experience is reported showing the improved effectiveness of the Combined Procedure when employed in this fashion when compared against the Tchebycheff and Aspiration Criterion Vector methods run separately.
Qingfu Zhang - One of the best experts on this subject based on the ideXlab platform.
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On Tchebycheff Decomposition Approaches for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation, 2018Co-Authors: Qingfu Zhang, Guangdong Tian, Junshan Yang, Zexuan ZhuAbstract:Tchebycheff decomposition represents one of the most widely used decomposition approaches that can convert a multiobjective optimization problem into a set of scalar optimization subproblems. Nevertheless, the geometric properties of the subproblem objective functions in Tchebycheff decomposition have not been explicitly studied. This paper proposes a Tchebycheff decomposition with ${l_{p}}$ -norm constraint on direction vectors in which the subproblem objective functions are endowed with clear geometric property. Especially, the Tchebycheff decomposition with ${l_{2}}$ -norm constraint on direction vectors is taken as an example to illustrate its advantage. A new unary ${R_{2}}$ indicator is also introduced to approximate the hyper-volume metric and justify the efficiency of the proposed Tchebycheff decomposition. A resultant Tchebycheff decomposition-based multiobjective evolutionary algorithm (MOEA) with ${l_{2}}$ -norm constraint and a new population update strategy is proposed to solve multiobjective optimization problems. The experimental results on both benchmark and real-world multiobjective optimization problems show that the proposed algorithm is capable of obtaining high quality solutions compared with other state-of-the-art MOEAs.
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moea d with nbi style Tchebycheff approach for portfolio management
Congress on Evolutionary Computation, 2010Co-Authors: Qingfu Zhang, Hui Li, Dietmar Maringer, Edward TsangAbstract:MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff approach, for MOEA/D to deal with such objectives. A portfolio management MOP has been used as an example to test the effectiveness of MOEA/D with NBI-style Tchebycheff approach.
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IEEE Congress on Evolutionary Computation - MOEA/D with NBI-style Tchebycheff approach for portfolio management
IEEE Congress on Evolutionary Computation, 2010Co-Authors: Qingfu Zhang, Dietmar Maringer, Edward TsangAbstract:MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff approach, for MOEA/D to deal with such objectives. A portfolio management MOP has been used as an example to test the effectiveness of MOEA/D with NBI-style Tchebycheff approach.
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Tchebycheff approximation in gaussian process model composition for multi objective expensive black box
World Congress on Computational Intelligence, 2008Co-Authors: Wudong Liu, Qingfu Zhang, Edward Tsang, Botond VirginasAbstract:Black-box expensive function is ubiquitous in real world problems. Much research has been done on scalar objective optimization for such problems with great success. Comparatively, very little work has been done in multi-objective optimization. In many cases, it is not straightforward to convert methods from scalar objective optimization to multi-objective optimization due to the complexities incurred by Pareto domination. In our pervious research, concept of model composition based on Gaussian Process metamodel and the powerful MOEA/D framework proved to be a successful approach for multi-objective optimization with black-box expensive functions. We derived Weighted-Sum and Tchebycheff model composition for bi-objective problems. However, due to the complexity of Tchebycheff decomposition structure, it is very hard, if not impossible, to extend the method to three or more objective problems in a nature way. In this paper, we propose an approximation method for Tchebycheff model composition which greatly simplify the derivation for three or more objective cases. Experiments show the approximation produces very similar performance as the Weighted-Sum and Tchebycheff without approximation. Thus, the new method enables us to tackle multi-objective problems with black-box expensive functions that could not be tackled effectively so far.
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IEEE Congress on Evolutionary Computation - Tchebycheff approximation in Gaussian Process model composition for multi-objective expensive black box
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Wudong Liu, Qingfu Zhang, Edward Tsang, Botond VirginasAbstract:Black-box expensive function is ubiquitous in real world problems. Much research has been done on scalar objective optimization for such problems with great success. Comparatively, very little work has been done in multi-objective optimization. In many cases, it is not straightforward to convert methods from scalar objective optimization to multi-objective optimization due to the complexities incurred by Pareto domination. In our pervious research, concept of model composition based on Gaussian Process metamodel and the powerful MOEA/D framework proved to be a successful approach for multi-objective optimization with black-box expensive functions. We derived Weighted-Sum and Tchebycheff model composition for bi-objective problems. However, due to the complexity of Tchebycheff decomposition structure, it is very hard, if not impossible, to extend the method to three or more objective problems in a nature way. In this paper, we propose an approximation method for Tchebycheff model composition which greatly simplify the derivation for three or more objective cases. Experiments show the approximation produces very similar performance as the Weighted-Sum and Tchebycheff without approximation. Thus, the new method enables us to tackle multi-objective problems with black-box expensive functions that could not be tackled effectively so far.
Edward Tsang - One of the best experts on this subject based on the ideXlab platform.
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moea d with nbi style Tchebycheff approach for portfolio management
Congress on Evolutionary Computation, 2010Co-Authors: Qingfu Zhang, Hui Li, Dietmar Maringer, Edward TsangAbstract:MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff approach, for MOEA/D to deal with such objectives. A portfolio management MOP has been used as an example to test the effectiveness of MOEA/D with NBI-style Tchebycheff approach.
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IEEE Congress on Evolutionary Computation - MOEA/D with NBI-style Tchebycheff approach for portfolio management
IEEE Congress on Evolutionary Computation, 2010Co-Authors: Qingfu Zhang, Dietmar Maringer, Edward TsangAbstract:MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff approach, for MOEA/D to deal with such objectives. A portfolio management MOP has been used as an example to test the effectiveness of MOEA/D with NBI-style Tchebycheff approach.
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Tchebycheff approximation in gaussian process model composition for multi objective expensive black box
World Congress on Computational Intelligence, 2008Co-Authors: Wudong Liu, Qingfu Zhang, Edward Tsang, Botond VirginasAbstract:Black-box expensive function is ubiquitous in real world problems. Much research has been done on scalar objective optimization for such problems with great success. Comparatively, very little work has been done in multi-objective optimization. In many cases, it is not straightforward to convert methods from scalar objective optimization to multi-objective optimization due to the complexities incurred by Pareto domination. In our pervious research, concept of model composition based on Gaussian Process metamodel and the powerful MOEA/D framework proved to be a successful approach for multi-objective optimization with black-box expensive functions. We derived Weighted-Sum and Tchebycheff model composition for bi-objective problems. However, due to the complexity of Tchebycheff decomposition structure, it is very hard, if not impossible, to extend the method to three or more objective problems in a nature way. In this paper, we propose an approximation method for Tchebycheff model composition which greatly simplify the derivation for three or more objective cases. Experiments show the approximation produces very similar performance as the Weighted-Sum and Tchebycheff without approximation. Thus, the new method enables us to tackle multi-objective problems with black-box expensive functions that could not be tackled effectively so far.
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IEEE Congress on Evolutionary Computation - Tchebycheff approximation in Gaussian Process model composition for multi-objective expensive black box
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Wudong Liu, Qingfu Zhang, Edward Tsang, Botond VirginasAbstract:Black-box expensive function is ubiquitous in real world problems. Much research has been done on scalar objective optimization for such problems with great success. Comparatively, very little work has been done in multi-objective optimization. In many cases, it is not straightforward to convert methods from scalar objective optimization to multi-objective optimization due to the complexities incurred by Pareto domination. In our pervious research, concept of model composition based on Gaussian Process metamodel and the powerful MOEA/D framework proved to be a successful approach for multi-objective optimization with black-box expensive functions. We derived Weighted-Sum and Tchebycheff model composition for bi-objective problems. However, due to the complexity of Tchebycheff decomposition structure, it is very hard, if not impossible, to extend the method to three or more objective problems in a nature way. In this paper, we propose an approximation method for Tchebycheff model composition which greatly simplify the derivation for three or more objective cases. Experiments show the approximation produces very similar performance as the Weighted-Sum and Tchebycheff without approximation. Thus, the new method enables us to tackle multi-objective problems with black-box expensive functions that could not be tackled effectively so far.
Minghe Sun - One of the best experts on this subject based on the ideXlab platform.
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robust optimization for interactive multiobjective programming with imprecise information applied to r d project portfolio selection
European Journal of Operational Research, 2014Co-Authors: Farhad Hassanzadeh, Hamid R Nemati, Minghe SunAbstract:A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems.
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robust optimization for interactive multiobjective programming with imprecise information applied to r d project portfolio selection
2013Co-Authors: Farhad Hassanzadeh, Hamid R Nemati, Minghe SunAbstract:A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer linear programming problems.
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Warm-Start Routines for Solving Augmented Weighted Tchebycheff Network Programs in Multiple-Objective Network Programming
INFORMS Journal on Computing, 2005Co-Authors: Minghe SunAbstract:Three warm-start routines are developed to find initial basic feasible solutions for augmented weighted Tchebycheff network programs, subproblems derived from multiple-objective network-programming problems. In an interactive solution procedure, a series of augmented weighted Tchebycheff network programs need to be solved sequentially to find representative nondominated solutions. To speed up the solution process using the network structure of the problem, these warm-start routines start the solution process of one augmented weighted Tchebycheff network program from the optimal solution of the previous one. All three warm-start routines use the same strategy but different ways of reducing the number of basic flow variables, or equivalently increasing the number of basic nonflow variables to construct a basic solution. These warm-start routines can be used by any interactive procedures to facilitate the solution process of multiple-objective network-programming problems. A detailed example is presented. A computational experiment is conducted to compare the performance of these warm-start routines. A cold-start routine and NETSIDE, specialized software for solving network problems with side constraints, are also used as references in the experiment. These warm-start routines can save substantial computation time.
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Procedures for Finding Nondominated Solutions for Multiple Objective Network Programming Problems
Transportation Science, 2003Co-Authors: Minghe SunAbstract:Procedures for finding nondominated solutions for multiple objective network programming problems are developed and tested. Nondominated solutions are obtained by solving augmented weighted Tchebycheff network programs. The procedures exploit the network structure of the problem in order to speed up the solution process. To use the network structure as much as possible, a weighted-sum network problem and/or a min-max network problem are solved in order to find a basic solution that is close to the optimal solution of the augmented weighted Tchebycheff network program. Starting from this basic solution, the special simplex method for network problems with side constraints is finally applied to solve the augmented weighted Tchebycheff network program. Computational results show that, for the test problems used in this study, up to 70% of computation time can be saved with the proposed procedures as compared with the sole application of the special simplex method for network problems with side constraints. These procedures can be incorporated into any interactive multiple-objective programming procedure which uses sample nondominated solutions to solve multiple-objective network programming problems.
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Interactive multiple objective programming using Tchebycheff programs and artificial neural networks
Computers & Operations Research, 2000Co-Authors: Minghe Sun, Antonie Stam, Ralph E. SteuerAbstract:A new interactive multiple objective programming procedure is developed that combines the strengths of the interactive weighted Tchebycheff procedure (Steuer and Choo. Mathematical Programming 1983;26(1):326–44.) and the interactive FFANN procedure (Sun, Stam and Steuer. Management Science 1996;42(6):835–49.). In this new procedure, nondominated solutions are generated by solving augmented weighted Tchebycheff programs (Steuer. Multiple criteria optimization: theory, computation and application. New York: Wiley, 1986.). The decision maker indicates preference information by assigning “values” to or by making pairwise comparisons among these solutions. The revealed preference information is then used to train a feed-forward artificial neural network. The trained feed-forward artificial neural network is used to screen new solutions for presentation to the decision maker on the next iteration. The computational experiments, comparing the current procedure with the interactive weighted Tchebycheff procedure and the interactive FFANN procedure, produced encouraging results
