An Improved NSGA-II Algorithm Based on Crowding Distance Elimination Strategy

Liu, Junhui; Chen, Xindu

doi:10.2991/ijcis.d.190328.001

Cited by 27 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another paper, NSGA-II is applied with crowding distance elimination strategy as appropriate for the tri-objective optimization problem under the specified constraints. 40…”

Section: Implementation Of Nsga-iimentioning

confidence: 99%

See 1 more Smart Citation

Optimization of a multi-objective lean assembly problem with reconfigurable machine cells

Pattanaik

Kant²

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Manufacturing sector is regularly facing the challenges caused due to high market dynamics and mass customization. Traditionally designed machine cells fail to address this issue due to lack of flexibility in capacity and functionality. However, the benefits of cell based production can be achieved by changing the machines involved and the design of cells. This paper presents a hybrid model of machine cells comprising of reconfigurable machine tools (RMTs) that are acting as part feeders for a lean assembly line of discrete products. The strategies of lean manufacturing to maintain the Takt time and synchronized one-piece-flow are considered in the model. A multi-objective optimization problem is formulated and solved to minimize the inter-cellular part movement, the error for Takt time among machine cells and the total reconfiguration time of the RMTs using NSGA-II metaheuristic. A numerical case example for the model is solved using MATLAB© and illustrated along with computational steps and Pareto optimal solutions.

show abstract

“…In another paper, NSGA-II is applied with crowding distance elimination strategy as appropriate for the tri-objective optimization problem under the specified constraints. 40…”

Section: Implementation Of Nsga-iimentioning

confidence: 99%

“…However, the drawbacks with this approach particularly for a triple objective problem like the present one is highlighted in Liu and Chen and Chen. 40 In order to overcome the non-uniform distribution of non-dominated solutions on the Pareto front, the crowding distance elimination approach is implemented here.…”

Section: Illustrative Examplementioning

confidence: 99%

Optimization of a multi-objective lean assembly problem with reconfigurable machine cells

Pattanaik

Kant²

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…When the rankings of the two non-dominant solutions are different, the solutions with the lower rank will be chosen. When two solutions fall within the same Pareto boundary, the solution with the larger crowding distance will be chosen [13].…”

Section: Crowding Distancementioning

confidence: 99%

Optimization of Waste Transportation Routes using Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) in the Eastern and Southern Regions of Bandung City, Indonesia

Afira¹,

Wijayanto²

2022

icdsos

View full text Add to dashboard Cite

Ensuring high-quality and effective urban waste management has been an important priority to achieve sustainable and environmental-friendly cities and communities mandated by Sustainable Development Goals (SDGs). The massively growing population in urban regions of developing countries, such as Bandung City, Indonesia, leads to the increasing volume of daily goods consumption and households waste production. The waste transportation route is one of the main determining factors for the cost of waste management. In this paper, we introduce the Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) to solve the waste transportation route optimization problem in the Eastern and Southern Regions of Bandung City, Indonesia. Compared to the existing traditional evolutionary algorithms, MNSGA-II offers three major important benefits: efficient computational complexity, no requirement of sharing parameters, and a non-elitism mechanism. Algorithm parameters include the number of generations, mutation rate, and crossover rate. Our extensive experiments suggest the best solution resulted in 14 routes with a total distance of 152,63 km. Further, our proposed route optimization is potentially beneficial to support the improvement of the sustainable waste management service system at Bandung City.

show abstract

“…NSGA-II adopts an elite strategy to accelerate the convergence speed. e proposed crowding distance sorting can better guarantee the diversity of the population, and the adoption of fast nondominated sorting method reduces the time complexity [30]. In order to improve the solving efficiency, NSGA-II algorithm is adopted in this paper to solve the established multi-objective optimization model.…”

Section: Design Of Solving Algorithm Based On Nsga-iimentioning

confidence: 99%

Control Strategy of Signal Transition after Emergency Vehicle Signal Preemption

Liu

Song

et al. 2020

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Emergency vehicle (EV) plays an important role in evacuations or rescues when emergencies occur. To insure that an EV can transfer people in danger to emergency shelters or medical assistance organizations as soon as possible, EV signal preemption (EVSP) strategy is usually adopted. After EV has passed through the intersection, traffic signal has to transfer back to normal signal timing scheme. This paper focuses on the control strategy of EV signal transitioning from EVSP back to normal operation. Considering both efficiency and fairness, the maximum vehicles passing through in per unit time during the transition period and the minimum difference between the maximum and the minimum queue length after transition are selected as objectives, and a multi-objective optimization model is presented. A nondominated sorting genetic algorithm II (NSGA-II) is designed to solve the optimization model and unique encoding and decoding methods are presented. The established model and designed algorithm are verified and the control effect is analyzed. Simulation results indicate that by adopting the control strategy obtained by the presented model, the number of vehicles passing through in per unit time during the transition period is increased and the difference of vehicle length in different directions is reduced significantly, from which we can conclude that the control method proposed in this paper has good performance.

show abstract

An Improved NSGA-II Algorithm Based on Crowding Distance Elimination Strategy

Cited by 27 publications

References 12 publications

Optimization of a multi-objective lean assembly problem with reconfigurable machine cells

Optimization of a multi-objective lean assembly problem with reconfigurable machine cells

Optimization of Waste Transportation Routes using Multi-objective Non-dominated Sorting Genetic Algorithm II (MNSGA-II) in the Eastern and Southern Regions of Bandung City, Indonesia

Control Strategy of Signal Transition after Emergency Vehicle Signal Preemption

Contact Info

Product

Resources

About