The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Panos Y Papalambros - One of the best experts on this subject based on the ideXlab platform.
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a sequential linear programming coordination algorithm for analytical target cascading
Design Automation Conference, 2007Co-Authors: Panos Y PapalambrosAbstract:Decomposition-based strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC that coordinates interactions among Subproblems using sequential linearizations. The linearity of Subproblems is maintained using infinity norms to measure deviations between targets and responses. A subproblem suspension strategy is used to suspend temporarily inclusion of Subproblems that do not need significant redesign, based on trust region and target value step size. An individual subproblem trust region method is introduced for faster convergence. The proposed strategy is intended for use in design optimization problems where sequential linearizations are typically effective, such as problems with extensive monotonicities, a large number of constraints relative to variables, and propagation of probabilities with normal distributions. Experiments with test problems show that, relative to standard ATC coordination, the number of subproblem evaluations is reduced considerably while the solution accuracy depends on the degree of monotonicity and nonlinearity.
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an augmented lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers
Structural and Multidisciplinary Optimization, 2006Co-Authors: S Tosserams, Panos Y Papalambros, L F P Etman, J E RoodaAbstract:Analytical target cascading is a method for design optimization of hierarchical, multilevel systems. A quadratic penalty relaxation of the system consistency constraints is used to ensure subproblem feasibility. A typical nested solution strategy consists of inner and outer loops. In the inner loop, the coupled Subproblems are solved iteratively with fixed penalty weights. After convergence of the inner loop, the outer loop updates the penalty weights. The article presents an augmented Lagrangian relaxation that reduces the computational cost associated with ill-conditioning of Subproblems in the inner loop. The alternating direction method of multipliers is used to update penalty parameters after a single inner loop iteration, so that Subproblems need to be solved only once. Experiments with four examples show that computational costs are decreased by orders of magnitude ranging between 10 and 1000.
Pitu B Mirchandani - One of the best experts on this subject based on the ideXlab platform.
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solving simultaneous route guidance and traffic signal optimization problem using space phase time hypernetwork
Transportation Research Part B-methodological, 2015Co-Authors: Pitu B Mirchandani, Xuesong ZhouAbstract:This paper addresses the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) where each vehicle in a traffic network is guided on a path and the traffic signals servicing these vehicles are set to minimize their travel times. The network is modeled as a space-phase-time (SPT) hyper-network to explicitly represent the traffic signal control phases and time-dependent vehicle paths. A Lagrangian-relaxation-based optimization framework is proposed to decouple the RGTSO problem into two Subproblems: the Route Guidance (RG) problem for multiple vehicles with given origins and destinations and the Traffic Signal Optimization (TSO) problem. In the RG subproblem, the route of each vehicle is provided subject to time-dependent link capacities imposed by the solution of the TSO problem, while the traffic signal timings are optimized according to the respective link travel demands aggregated from the vehicle trajectories. The dual prices of the RG subproblem indicate search directions for optimization of the traffic signal phase sequences and durations in the TSO subproblem. Both RG and TSO Subproblems can be solved using a computationally efficient finite-horizon dynamic programming framework, enhanced by parallel computing techniques. Two numerical experiments demonstrated that the system optimum of the RGTSO problem can be quickly reached with relatively small duality gap for medium-size urban networks.
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a real time traffic signal control system architecture algorithms and analysis
Transportation Research Part C-emerging Technologies, 2001Co-Authors: Pitu B Mirchandani, Larry HeadAbstract:Abstract The paper discusses a real-time traffic-adaptive signal control system referred to as RHODES. The system takes as input detector data for real-time measurement of traffic flow, and “optimally” controls the flow through the network. The system utilizes a control architecture that (1) decomposes the traffic control problem into several Subproblems that are interconnected in an hierarchical fashion, (2) predicts traffic flows at appropriate resolution levels (individual vehicles and platoons) to enable pro-active control, (3) allows various optimization modules for solving the hierarchical Subproblems, and (4) utilizes a data structure and computer/communication approaches that allow for fast solution of the Subproblems, so that each decision can be downloaded in the field appropriately within the given rolling time horizon of the corresponding subproblem. The RHODES architecture, algorithms, and its analysis are presented. Laboratory test results, based on implementation of RHODES on simulation models of actual scenarios, illustrate the effectiveness of the system.
Qingfu Zhang - One of the best experts on this subject based on the ideXlab platform.
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are all the Subproblems equally important resource allocation in decomposition based multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation, 2016Co-Authors: Aimin Zhou, Qingfu ZhangAbstract:Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective Subproblems and solve them in a collaborative way. A naive way to distribute computational effort is to treat all the Subproblems equally and assign the same computational resource to each subproblem. This paper proposes a generalized resource allocation (GRA) strategy for decomposition-based MOEAs by using a probability of improvement vector. Each subproblem is chosen to invest according to this vector. An offline measurement and an online measurement of the subproblem hardness are used to maintain and update this vector. Utility functions are proposed and studied for implementing a reasonable and stable online resource allocation strategy. Extensive experimental studies on the proposed GRA strategy have been conducted.
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interrelationship based selection for decomposition multiobjective optimization
IEEE Transactions on Systems Man and Cybernetics, 2015Co-Authors: Sam Kwong, Qingfu Zhang, Kalyanmoy DebAbstract:Multiobjective evolutionary algorithm based on decomposition (MOEA/D), which bridges the traditional optimization techniques and population-based methods, has become an increasingly popular framework for evolutionary multiobjective optimization. It decomposes a multiobjective optimization problem (MOP) into a number of optimization Subproblems. Each subproblem is handled by an agent in a collaborative manner. The selection of MOEA/D is a process of choosing solutions by agents. In particular, each agent has two requirements on its selected solution: one is the convergence toward the efficient front, the other is the distinction with the other agents’ choices. This paper suggests addressing these two requirements by defining mutual-preferences between Subproblems and solutions. Afterwards, a simple yet effective method is proposed to build an interrelationship between Subproblems and solutions, based on their mutual-preferences. At each generation, this interrelationship is used as a guideline to select the elite solutions to survive as the next parents. By considering the mutual-preferences between Subproblems and solutions (i.e., the two requirements of each agent), the selection operator is able to balance the convergence and diversity of the search process. Comprehensive experiments are conducted on several MOP test instances with complicated Pareto sets. Empirical results demonstrate the effectiveness and competitiveness of our proposed algorithm.
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stable matching based selection in evolutionary multiobjective optimization
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Qingfu Zhang, Sam Kwong, Ran WangAbstract:Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization Subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for Subproblems can be regarded as a matching between Subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in.
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decomposition of a multiobjective optimization problem into a number of simple multiobjective Subproblems
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Fangqing Gu, Qingfu ZhangAbstract:This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization Subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these Subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better.
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expensive multiobjective optimization by moea d with gaussian process model
IEEE Transactions on Evolutionary Computation, 2010Co-Authors: Qingfu Zhang, Wudong Liu, Edward Tsang, Botond VirginasAbstract:In some expensive multiobjective optimization problems (MOPs), several function evaluations can be carried out in a batch way. Therefore, it is very desirable to develop methods which can generate multipler test points simultaneously. This paper proposes such a method, called MOEA/D-EGO, for dealing with expensive multiobjective optimization. MOEA/D-EGO decomposes an MOP in question into a number of single-objective optimization Subproblems. A predictive model is built for each subproblem based on the points evaluated so far. Effort has been made to reduce the overhead for modeling and to improve the prediction quality. At each generation, MOEA/D is used for maximizing the expected improvement metric values of all the Subproblems, and then several test points are selected for evaluation. Extensive experimental studies have been carried out to investigate the ability of the proposed algorithm.
Xuesong Zhou - One of the best experts on this subject based on the ideXlab platform.
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solving simultaneous route guidance and traffic signal optimization problem using space phase time hypernetwork
Transportation Research Part B-methodological, 2015Co-Authors: Pitu B Mirchandani, Xuesong ZhouAbstract:This paper addresses the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) where each vehicle in a traffic network is guided on a path and the traffic signals servicing these vehicles are set to minimize their travel times. The network is modeled as a space-phase-time (SPT) hyper-network to explicitly represent the traffic signal control phases and time-dependent vehicle paths. A Lagrangian-relaxation-based optimization framework is proposed to decouple the RGTSO problem into two Subproblems: the Route Guidance (RG) problem for multiple vehicles with given origins and destinations and the Traffic Signal Optimization (TSO) problem. In the RG subproblem, the route of each vehicle is provided subject to time-dependent link capacities imposed by the solution of the TSO problem, while the traffic signal timings are optimized according to the respective link travel demands aggregated from the vehicle trajectories. The dual prices of the RG subproblem indicate search directions for optimization of the traffic signal phase sequences and durations in the TSO subproblem. Both RG and TSO Subproblems can be solved using a computationally efficient finite-horizon dynamic programming framework, enhanced by parallel computing techniques. Two numerical experiments demonstrated that the system optimum of the RGTSO problem can be quickly reached with relatively small duality gap for medium-size urban networks.
Larry Head - One of the best experts on this subject based on the ideXlab platform.
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a real time traffic signal control system architecture algorithms and analysis
Transportation Research Part C-emerging Technologies, 2001Co-Authors: Pitu B Mirchandani, Larry HeadAbstract:Abstract The paper discusses a real-time traffic-adaptive signal control system referred to as RHODES. The system takes as input detector data for real-time measurement of traffic flow, and “optimally” controls the flow through the network. The system utilizes a control architecture that (1) decomposes the traffic control problem into several Subproblems that are interconnected in an hierarchical fashion, (2) predicts traffic flows at appropriate resolution levels (individual vehicles and platoons) to enable pro-active control, (3) allows various optimization modules for solving the hierarchical Subproblems, and (4) utilizes a data structure and computer/communication approaches that allow for fast solution of the Subproblems, so that each decision can be downloaded in the field appropriately within the given rolling time horizon of the corresponding subproblem. The RHODES architecture, algorithms, and its analysis are presented. Laboratory test results, based on implementation of RHODES on simulation models of actual scenarios, illustrate the effectiveness of the system.
