1822 Discussion on Search Strategy for Multi-objective Genetic Algorithm with Consideration of Accuracy and Broadness of Pareto Optimal Solutions

2007 ◽  
Vol 2007.20 (0) ◽  
pp. 369-370
Author(s):  
Masashi NISHIOKA ◽  
Tomoyuki HIROYASU ◽  
Mitsunori MIKI
Keyword(s):  
Genetic Algorithm ◽  
Search Strategy ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

2019 ◽  
Vol 9 (3) ◽  
pp. 37-57
Author(s):  
Youssef Harrath ◽  
Rashed Bahlool
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Lower Bound ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Mutation Operators ◽  
Multi Objective Genetic Algorithm

The problem of allocating real-time tasks to cloud computing resources minimizing the makespan is defined as a NP-hard problem. This work studies the same problem with two realistic multi-objective criteria; the makespan and the total cost of execution and communication between tasks. A mathematical model including objective functions and constraints is proposed. In addition, a theoretical lower bound for the makespan which served later as a baseline to benchmark the experimental results is theoretically determined and proven. To solve the studied problem, a multi-objective genetic algorithm is proposed in which new crossover and mutation operators are proposed. Pareto-optimal solutions are retrieved using the genetic algorithm. The experimental results show that genetic algorithm provides efficient solutions in term of makespan for different-size problem instances with reference to the lower bound. Moreover, the proposed genetic algorithm produces many Pareto optimal solutions that dominate the solution given by greedy algorithm for both criteria.

Analysis and Modeling of Supply Chain Management of Fresh Products Based on Genetic Algorithm in E-commerce Environment

2020 ◽  
Author(s):  
Yaoting Chen
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Weighted Sum ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Process ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Proposed Model ◽  
Fresh Products

Abstract BackgroundSupply chain provides the chance to enhance chain performances by decrease these uncertainties. It is a demand for some level of co-ordination of activities and processes within and between organization in the supply chain to decrease uncertainties and increase more cost for customers. Partner selection is an important issue in the supply chain management of fresh products in E-commerce environment. In this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. ResultsThe proposed multi-objective genetic algorithm is to search the set of Pareto-optimal solutions for these conflicting objectives using by weighted sum approach. The proposed model suitable for fresh products in E-commerce environment to optimize supply chain are derived. The value of objective 1 (f1) performs approximately nonlinearly with the increasing the value of objective 2,3 and 4 (f2,f3 and f4). At the value of objective 1 of 3.2*105, f2, f3 and f4 is about 4.3*105, 86 and 5.6*104. When the value of objective 1 is increased to 7.6*105, the minimum f2, f3 and f4 is about 3.0*105, 38 and 2.56*104. It is noted that the value of objective 1 is increased from 6.4*105 to 7.6*105, the variation of f2, f3 and f4 is 11.7%, 17.4% and 3.4% respectively. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product in E-commerce environment.ConclusionsIn this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. Four objectives for optimal process are included in the proposed model: (1) maximization of green appraisal score, (2) minimization of transportation time and total time comprised of product time, (3) maximization of average product quality, (4) minimization of transportation cost and total cost comprised of product cost. In order to evaluate optimal process, set of Pareto-optimal solutions is obtained based on the weighted sum method.

Topology Optimization of Multi-Component Structures via Decomposition-Based Assembly Synthesis

2003 ◽  
Author(s):  
Naesung Lyu ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Structural Performance ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Structural Stiffness ◽  
Optimal Solutions ◽  
Ground Structure ◽  
Multi Objective Genetic Algorithm ◽  
Trade Offs

A method is presented for synthesizing multi-component structural assemblies with maximum structural performance and manufacturability. The problem is posed as a relaxation of decomposition-based assembly synthesis [1,2,3], where both topology and decomposition of a structure are regarded as variables over a ground structure with non-overlapping beams. A multi-objective genetic algorithm [4,5] with graph-based crossover [6,7,8], coupled with FEM analyses, is used to obtain Pareto optimal solutions to this problem, exhibiting trade-offs among structural stiffness, total weight, component manufacturability (size and simplicity), and the number of joints. Case studies with a cantilever and a simplified automotive floor frame are presented, and representative designs in the Pareto front are examined for the trade-offs among the multiple criteria.

Design Optimization of a Centrifugal Compressor Impeller by Multi-Objective Genetic Algorithm

2009 ◽  
Author(s):  
Jin-Hyuk Kim ◽  
Jae-Ho Choi ◽  
Afzal Husain ◽  
Kwang-Yong Kim
Keyword(s):  
Genetic Algorithm ◽  
Centrifugal Compressor ◽  
Surrogate Model ◽  
Total Pressure ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Compressor Impeller

This paper presents design optimization of a centrifugal compressor impeller with hybrid multi-objectives evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The surrogate model, Radial Basis Neural Network is trained on the numerical solutions by carrying out leave-one-out cross-validation for the data set. The trade-off between the two objectives has been found out and discussed in light of the Pareto-optimal solutions. The optimization results show that isentropic efficiencies and total pressure ratios of the cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

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