scholarly journals Numerical Experiments on Size Optimization of Truss Structures by Use of Genetic Algorithm (An Approach for Optimization Using Fully Stressed Designand Genetic Algorithm).

1996 ◽  
Vol 62 (597) ◽  
pp. 1234-1241 ◽  
Author(s):  
Masateru ASAYAMA ◽  
Hiroshi HASEGAWA ◽  
Keishi KAWAMO
Keyword(s):  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Truss Structures ◽  
Size Optimization

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

scholarly journals Integrated Harvest and Farm-to-Door Distribution Scheduling with Postharvest Quality Deterioration for Vegetable Online Retailing

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 724
Author(s):  
Yiping Jiang ◽  
Bei Bian ◽  
Lingling Li
Keyword(s):  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Programming Model ◽  
Time Windows ◽  
Sensitivity Analyses ◽  
Postharvest Quality ◽  
Online Retailing ◽  
Quality Deterioration ◽  
Distribution Scheduling ◽  
Proposed Model

With the rise of vegetable online retailing in recent years, the fulfillment of vegetable online orders has been receiving more and more attention. This paper addresses an integrated optimization model for harvest and farm-to-door distribution scheduling for vegetable online retailing. Firstly, we capture the perishable property of vegetables, and model it as a quadratic postharvest quality deterioration function. Then, we incorporate the postharvest quality deterioration function into the integrated harvest and farm-to-door distribution scheduling and formulate it as a quadratic vehicle routing programming model with time windows. Next, we propose a genetic algorithm with adaptive operators (GAAO) to solve the model. Finally, we carry out numerical experiments to verify the performance of the proposed model and algorithm, and report the results of numerical experiments and sensitivity analyses.

Feeder Ship Size Optimization in Maritime Markets with Uncertainty

2021 ◽  
Vol 38 (03) ◽  
pp. 2040014
Author(s):  
Hongtao Hu ◽  
Jiao Mo ◽  
Lufei Huang
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Planning Horizon ◽  
Dynamic Programming Method ◽  
Size Optimization ◽  
Stochastic Dynamic ◽  
Shipping Company ◽  
Decision Behaviors

This paper considers the ship size optimization problem for a liner shipping company that provides feeder service between one hub port and one feeder port. In the maritime market with uncertainty, this problem becomes more challenging. This research first analyzes the decision behaviors of the shipping company. Then, a stochastic dynamic programming method is proposed to calculate the expected total volume of containers transported within the planning horizon. Using the calculated volumes as input parameters calculate the profit of each ship sizes and then determine the suitable ship size for the feeder route. Numerical experiments are performed to validate the effectiveness of the proposed method and the efficiency of the proposed algorithm.

scholarly journals Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-31 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Optimal Strategy ◽  
Numerical Experiments ◽  
Solution Method ◽  
Autonomous Systems ◽  
Traveling Salesman ◽  
Optimal Solutions

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

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