scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF SNAKE ROBOT IN SERPENTINE LOCOMOTION

2021 ◽  
Vol 22 (2) ◽  
pp. 364-383
Author(s):  
Md Raisuddin Khan ◽  
Marwan Badran ◽  
Siti Fauziah ◽  
Zulkifly Bin Zainal Abidin
Keyword(s):  
Energy Consumption ◽  
Periodic Pattern ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Snake Robot ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Travelling Time ◽  
Simulation Results ◽  
Winding Angle

This paper presents multi-objective optimization for a snake robot with serpentine locomotion. Genetic algorithm (GA) is used to achieve two objectives: minimizing the total travelling time and minimizing the total energy consumption. The effect of initial values of winding angle and acceleration on energy consumption and average speed is depicted. The simulation results show a periodic pattern of the joint torques when the robot maintains a serpenoid curve during travel. Moreover, a Pareto-optimal front was generated for optimal solutions of both of the objectives, while the weighted sum method was used for selecting the best solution. Finally, the simulation results were verified experimentally on an eight-link snake robot considering the limitations of the servomotors used in the experiment. The experimental results with the winding angle of 30° was found as the optimum winding angle that can achieve both objectives of minimizing the energy consumption and the travelling time. ABSTRAK: Kajian ini berkenaan pelbagai-objektif optimum bagi robot ular dengan gerakan serpentin. Algoritma genetik (GA) diguna bagi mencapai dua objektif ini iaitu mengurangkan jumlah masa gerakan dan guna tenaga. Gambaran kesan awal nilai sudut belitan dan pecutan pada guna tenaga dan purata kelajuan dihasilkan. Dapatan simulasi menunjukkan corak berkala tork sendi yang tetap terhasil semasa robot ini berkeadaan lengkung serpenoid ketika bergerak. Tambahan, Pareto-optimal berdepan terhasil bagi solusi optimum pada kedua-dua objektif, sementara kaedah berat campuran digunakan bagi menentukan solusi terbaik. Akhirnya, dapatan simulasi disahkan secara eksperimen pada robot ular lapan-bahagian dengan menimbangkan kekurangan servomotor yang digunakan dalam eksperimen. Dapatan eksperimen menunjukkan sudut belitan 30° adalah sudut belitan optimum bagi kedua-dua objektif iaitu mengurangkan tenaga dan masa gerakan.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

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