scholarly journals Assessing Solution Quality in Stochastic Programs via Sampling

2009 ◽  
pp. 102-122 ◽  
Author(s):  
Güzin Bayraksan ◽  
David P. Morton
Keyword(s):  
Solution Quality ◽  
Stochastic Programs

Partial Benders Decomposition: General Methodology and Application to Stochastic Network Design

2020 ◽  
Author(s):  
Teodor Gabriel Crainic ◽  
Mike Hewitt ◽  
Francesca Maggioni ◽  
Walter Rei
Keyword(s):  
Network Design ◽  
Solution Method ◽  
Benders Decomposition ◽  
Solution Process ◽  
Master Problem ◽  
Solution Quality ◽  
Stochastic Programs ◽  
Multicommodity Network Design ◽  
Planning Problems ◽  
Stability Of The Solution

Benders decomposition is a broadly used exact solution method for stochastic programs, which has been increasingly applied to solve transportation and logistics planning problems under uncertainty. However, this strategy comes with important drawbacks, such as a weak master problem following the relaxation step that confines the dual cuts to the scenario subproblems. In this paper, we propose a partial Benders decomposition methodology, based on the idea of including explicit information from the scenario subproblems in the master. To investigate the benefits of this methodology, we apply it to solve a general class of two-stage stochastic multicommodity network design models. Specifically, we solve the challenging variant of the model where both the demands and the arc capacities are stochastic. Through an extensive experimental campaign, we clearly show that the proposed methodology yields significant benefits in computational efficiency, solution quality, and stability of the solution process.

scholarly journals Assessing solution quality in stochastic programs

2006 ◽  
Vol 108 (2-3) ◽  
pp. 495-514 ◽  
Author(s):  
Güzin Bayraksan ◽  
David P. Morton
Keyword(s):  
Solution Quality ◽  
Stochastic Programs

Monte Carlo bounding techniques for determining solution quality in stochastic programs

1999 ◽  
Vol 24 (1-2) ◽  
pp. 47-56 ◽  
Author(s):  
Wai-Kei Mak ◽  
David P. Morton ◽  
R.Kevin Wood
Keyword(s):  
Monte Carlo ◽  
Solution Quality ◽  
Stochastic Programs

scholarly journals Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter

2021 ◽  
Vol 11 (3) ◽  
pp. 1211
Author(s):  
En-Chih Chang ◽  
Chun-An Cheng ◽  
Rong-Ching Wu
Keyword(s):  
Steady State ◽  
Digital Signal Processor ◽  
Tracking Control ◽  
Harmonic Distortion ◽  
Digital Signal ◽  
Fast Convergence ◽  
Solution Quality ◽  
Hybrid Strategy ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

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