Multi-objective transportation network design: Accelerating search by applying ε-NSGAII
نویسندگان
چکیده
The optimization of infrastructure planning in a multimodal passenger transportation network is formulated as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train stations and the frequency of public transport lines. For a real life case study the Pareto set is estimated by the Epsilon Non-dominated Sorting Genetic Algorithm (ε-NSGAII), since due to high computation time a high performance within a limited number of evaluated solutions is desired. As a benchmark, the NSGAII is used. In this paper Pareto sets from runs of both algorithms are analyzed and compared. The results show that after a reasonable computation time, ε-NSGAII outperforms NSGAII for the most important indicators, especially in the early stages of algorithm executions.
منابع مشابه
The Value of Online Adaptive Search: A Performance Comparison of NSGAII, ε-NSGAII and εMOEA
This paper demonstrates how adaptive population-sizing and epsilon-dominance archiving can be combined with the Nondominated Sorted Genetic Algorithm-II (NSGAII) to enhance the algorithm’s efficiency, reliability, and ease-of-use. Four versions of the enhanced Epsilon Dominance NSGA-II (ε-NSGAII) are tested on a standard suite of evolutionary multiobjective optimization test problems. Comparati...
متن کاملA mathematical multi-objective model for treatment network design (physical-biological-thermal) using modified NSGA II
Today, sustainable development is one of the important issues in regard to the economy of a country. This issue magnifies the necessity for increased scrutiny towards issues such as environmental considerations and product recovery in closed-loop supply chains (CLSCs). The most important motivational factors influencing research on these topics can be considered in two general groups: environme...
متن کاملMulti-objective Optimization of Multimodal Passenger Transportation Networks: Coping with Demand Uncertainty
Robustness of optimal solutions when solving network design problems is of great importance because of uncertainty in future demand. In this research the optimization of infrastructure planning in a multimodal passenger transportation network is defined as a multiobjective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectiv...
متن کاملHybrid Pareto archived dynamically dimensioned search for multi-objective combinatorial optimization: application to water distribution network design
Pareto archived dynamically dimensioned search (PA-DDS) has been modified to solve combinatorial multi-objective optimization problems. This new PA-DDS algorithm uses discrete-DDS as a search engine and archives all non-dominated solutions during the search. PA-DDS is also hybridized by a general discrete local search strategy to improve its performance near the end of the search. PA-DDS inheri...
متن کاملInterval-based Solar PV Power Forecasting Using MLP-NSGAII in Niroo Research Institute of Iran
This research aims to predict PV output power by using different neuro-evolutionary methods. The proposed approach was evaluated by a data set, which was collected at 5-minute intervals in the photovoltaic laboratory of Niroo Research Institute of Iran (Tehran). The data has been divided into three intervals based on the amount of solar irradiation, and different neural networks were used for p...
متن کامل