Multi-objective Memetic Algorithm Based on NSGA-II and Simulated Annealing for Calibrating CORSIM Micro-Simulation Models of Vehicular Traffic Flow

Cobos, Carlos; Erazo, Cristian; Luna, Julio; Mendoza, Martha; Gaviria, Carlos; Arteaga, Cristian; Paz, Alexander

doi:10.1007/978-3-319-44636-3_44

Cited by 15 publications

(12 citation statements)

References 8 publications

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“…These values may not be obvious to determine, but rather might be determined by trial and error for a given problem [40]. In this study, values assigned to the optimization parameters were determined using experience gained from previous research [45][46][47][48][49][50][51] that involved SA and other comparable algorithms. Table 3 lists the parameter values used in this study.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Simultaneous Generation of Optimum Pavement Clusters and Associated Performance Models

Khadka

Paz

Arteaga

et al. 2018

Mathematical Problems in Engineering

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With regard to developing pavement performance models (PPMs), the existing state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and associated PPMs simultaneously. However, the approach does not determine optimal clustering to minimize error; that is, the number of clusters and explanatory variables are prespecified to determine the corresponding coefficients of the PPMs. In addition, existing formulations do no address issues associated with overfitting as there is no limit to include parameters in the model. In order to address this limitation, this paper proposes a mathematical program within the CLR approach to determine simultaneously (1) an optimal number of clusters, (2) assignment of segments into clusters, and (3) regression coefficients for all prespecified explanatory variables required to minimize the estimation error. The Bayesian Information Criteria is proposed to limit the number of optimal clusters. A simulated annealing coupled with ordinary least squares was used to solve the mathematical program.

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Section: Experiments and Resultsmentioning

confidence: 99%

Simultaneous Generation of Optimum Pavement Clusters and Associated Performance Models

Khadka

Paz

Arteaga

et al. 2018

Mathematical Problems in Engineering

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“…A common calibration criterion from the literature was used in this study [2], [8], [22], [38]: the absolute difference between actual and simulated link counts and speeds should be less than 15% for at least 85% of the links. In addition, the GEH [40] is required to be less than 5 for at least 85% of the links.…”

Section: A Calibration Criterionmentioning

confidence: 99%

“…The performance of the proposed solution algorithm is compared with the following alternatives which were used recently and successfully to solve the same calibration problem [2], [8], [22], [38]:  Mono-objective algorithms,  Single-state multi-objective algorithms, also used as local search strategies in memetic algorithms, and  Memetic multi-objective algorithms. All algorithms were implemented to take advantage of parallel computing using multi-threading.…”

Section: Alternative Algorithmsmentioning

confidence: 99%

“…According to several authors [11], [16], [17], the most popular multi-objective optimization algorithms are NSGA-II [18] and SPEA-2 [19], which are relatively easy to implement [20], [21]. Cobos, et al [22] proposed a multiobjective memetic algorithm based on NSGA-II and SA, NSGA-II-SA, for the calibration of microscopic traffic flow simulation models. NSGA-II provided exploration while SA performed exploitation.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

et al. 2020

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The calibration of traffic-flow simulation models continues to be a significant problem without a generalized, comprehensive, and low-cost solution. Existing calibration approaches either have not explicitly addressed the multi-objective characteristics of the problem or determining their hyperparameters requires significant effort. In addition, statistical evaluation of alternative solution algorithms is not performed to ensure dominance and stability. This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic traffic-flow simulation models. The adapted MOGBHS provides five key capabilities for solving the proposed problem including 1) consideration of multiple objectives, 2) easily extendable to memetic versions, 3) simultaneous consideration of continuous and discrete variables, 4) efficient ordering of no dominated solutions, 5) relatively easy tuning of hyperparameters, and 6) easily parallelization to maximize exploration and exploitation without increasing computing time. Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem. The efficiency and effectiveness of the algorithms were tested based on convergence, minimization of errors, calibration criterion, and two statistical nonparametric tests. The proposed approach dominated all alternative algorithms in all cases and provided the most stable and diverse solutions.

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“…In Tables 7 and 8, we can see that the data obtained by NSGA-II hybrid with SA are more effective, thus the results obtained by this algorithm were selected as the data source of knowledge mining. More comparisons between this algorithm and other algorithms can be seen in [38].…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

Qiu

Zhang

2019

Applied Sciences

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Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. First, through offline machine learning and data mining, attributes of operations are selected to mine the initial population knowledge. Second, an addition–deletion sorting method (ADSM) is proposed to reprioritize operations and then form the rule-based initial population. Finally, the nondominated sorting genetic algorithm II (NSGA-II) hybrid with simulated annealing is used to obtain the Pareto solutions. To evaluate the effectiveness of the proposed method, three other types of initial populations were considered under different iterations and population sizes. The experimental results demonstrate that the new approach has a good performance in solving the multi-variety production planning problems, whether it is the function value or the performance metric of the acquired Pareto solutions.

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Multi-objective Memetic Algorithm Based on NSGA-II and Simulated Annealing for Calibrating CORSIM Micro-Simulation Models of Vehicular Traffic Flow

Cited by 15 publications

References 8 publications

Simultaneous Generation of Optimum Pavement Clusters and Associated Performance Models

Simultaneous Generation of Optimum Pavement Clusters and Associated Performance Models

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

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