A Multi-objective Optimization Model for Location Allocation of Railway Cargo Storage and Its Evolutionary Algorithm Design

2021 ◽  
pp. 119-128
Author(s):  
Feiyu Yang ◽  
Shaoquan Ni
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Model ◽  
Algorithm Design ◽  
Multi Objective Optimization ◽  
Location Allocation ◽  
Multi Objective

scholarly journals Multi-Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Ming Zhong ◽  
RenNong Yang ◽  
Jun Wu ◽  
Huan Zhang
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Model ◽  
Population Distribution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Target Value ◽  
Gaussian Perturbation

This study proposes a path-finding model for multi-target strike planning. The model evaluates three elements, i.e., the target value, the aircraft’s threat tolerance, and the battlefield threat, and optimizes the striking path by constraining the balance between mission execution and the combat survival. In order to improve the speed of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), we use the conjugate gradient method for optimization. A Gaussian perturbation is added to the search points to make their distribution closer to the population distribution. The simulation shows that the proposed method effectively chooses its target according to the target value and the aircraft’s acceptable threat value, completes the strike on high value targets, evades threats, and verifies the feasibility and effectiveness of the multi-objective optimization model.

Multi-Objective Optimization Model of Activity Network and Its Evolutionary Algorithm Based on The Pareto Optimal

2013 ◽  
Vol 8 (5) ◽  
pp. 1119-1127
Author(s):  
Zhenqiang Bao ◽  
Yu Xu ◽  
Junwu Zhu ◽  
Xiaohong Huang ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Model ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Activity Network ◽  
Multi Objective

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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