scholarly journals A Study on Multi-objective Vehicle Rounting Problem considering Customer Satisfaction with Due-time : The Creation of Pareto Optimal Solutions by Hybrid Genetic Algorithm

1998 ◽  
Vol 64 (619) ◽  
pp. 1108-1115 ◽  
Author(s):  
Weerapat SESSOMBOON ◽  
Kei WATANABE ◽  
Takashi IROHARA ◽  
Kazuho YOSHIMOTO
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Hybrid Genetic Algorithm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
The Creation

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.

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.

scholarly journals Single- and Multi-Objective Optimization of a Dual-Chamber Microbial Fuel Cell Operating in Continuous-Flow Mode at Steady State

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 839
Author(s):  
Ibrahim M. Abu-Reesh
Keyword(s):  
Flow Rate ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Dual Chamber ◽  
Worst Case ◽  
Multi Objective ◽  
Model Based ◽  
Conflicting Objectives

Microbial fuel cells (MFCs) are a promising technology for bioenergy generation and wastewater treatment. Various parameters affect the performance of dual-chamber MFCs, such as substrate flow rate and concentration. Performance can be assessed by power density ( PD ), current density ( CD ) production, or substrate removal efficiency ( SRE ). In this study, a mathematical model-based optimization was used to optimize the performance of an MFC using single- and multi-objective optimization (MOO) methods. Matlab’s fmincon and fminimax functions were used to solve the nonlinear constrained equations for the single- and multi-objective optimization, respectively. The fminimax method minimizes the worst-case of the two conflicting objective functions. The single-objective optimization revealed that the maximum PD ,   CD , and SRE were 2.04 W/m2, 11.08 A/m2, and 73.6%, respectively. The substrate concentration and flow rate significantly impacted the performance of the MFC. Pareto-optimal solutions were generated using the weighted sum method for maximizing the two conflicting objectives of PD and CD in addition to PD and SRE   simultaneously. The fminimax method for maximizing PD and CD showed that the compromise solution was to operate the MFC at maximum PD conditions. The model-based optimization proved to be a fast and low-cost optimization method for MFCs and it provided a better understanding of the factors affecting an MFC’s performance. The MOO provided Pareto-optimal solutions with multiple choices for practical applications depending on the purpose of using the MFCs.

A method of multiobjective optimization using a genetic algorithm and an artificial immune system

2009 ◽  
Vol 223 (5) ◽  
pp. 1243-1252 ◽  
Author(s):  
H Park ◽  
N-S Kwak ◽  
J Lee
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Immune System ◽  
Artificial Immune System ◽  
Affinity Maturation ◽  
Pareto Optimal ◽  
Mathematical Function ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Artificial Immune

The immune system has pattern recognition capabilities based on reinforced learning, memory, and affinity maturation interacting between antigens (Ags) and antibodies (Abs). This article deals with an adaptation of artificial immune system (AIS) into genetic-algorithm (GA)-based multi-objective optimization. The present study utilizes the pattern recognition from an AIS and the evolution from a GA. Using affinity measures between Ags and Abs, GA-based immune simulation discovers a generalist Ab that represents the common pattern among Ags. Non-dominated Pareto-optimal solutions are obtained via GA-based immune simulation in which dominated designs are considered as Ags, whereas non-dominated designs are assigned to Abs. This article discusses the procedure of identifying Pareto-optimal solutions through the immune system-based pattern recognition. A number of mathematical function problems that are described by discontinuity or disconnection in the shape of Pareto surface are first examined as test examples. Subsequently, engineering optimization problems such as rotating flywheel disc and ten-bar planar truss are explored to support the present study.

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