Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G Multi-User Massive MIMO

2020 ◽  
pp. 1-20
Author(s):  
K. E. Purushothaman ◽  
V. Nagarajan
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Optimization Algorithm ◽  
Massive Mimo ◽  
Deep Neural Network ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Hybrid Algorithm using the Advantage of Krill Herd Algorithm with Opposition- Based Learning for Dynamic Resource Allocation in Cloud Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 306-311
Keyword(s):  
Resource Allocation ◽  
Optimization Algorithm ◽  
Computational Cost ◽  
Dynamic Resource Allocation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Opposition Based Learning ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Dynamic Resource

The cloud computing systems have more consideration due to the growing control for elevated concert computing and data storage. Resource allocation plays a vital role in cloud systems. To overcome the obscurity present in resources allocation system. In this paper, we design and develop a technique for dynamic resource allocation. A Hybridized approach is designed with the help of multi-objective oppositional krill herd optimization algorithm (OKHA). It is a combination of the krill herd algorithm and Opposition-based learning (OBL), OBL is added to get enhanced performance of the krill herd algorithm. The objective of this hybridization is to reduce the cost. In this Hybridized process each task consists of two cost i.e monetary cost and computational cost. Here each task is divided into many subtasks and assigns the respective resources to it. Our proposed multi-objective optimization algorithm will decide allocation of resource for the each subtask in this process. Finally, the testing is passed out, we evaluate our proposed algorithm with PSO, and GA algorithm we verified the performance levels of our proposed Multi-objective optimization algorithm.

scholarly journals New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

2019 ◽  
Vol 39 (4) ◽  
pp. 850-865
Author(s):  
Dinh-Nam Dao ◽  
Li-Xin Guo
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Optimization Problem ◽  
Mean Square Displacement ◽  
Mean Square ◽  
Stiffness Parameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Engine Mount

In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep Neural Network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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