scholarly journals Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1882
Author(s):  
Longda Wang ◽  
Xingcheng Wang ◽  
Kaiwei Liu ◽  
Zhao Sheng
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimization Algorithms ◽  
Hybrid Optimization ◽  
Multi Objective ◽  
Chaotic Mapping ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Train Operation ◽  
Automatic Train Operation

Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

Adaptive neural network with hybrid optimization oriented localization in wireless sensor network: A multi-objective model

2020 ◽  
pp. 2050064
Author(s):  
P. Purusothaman ◽  
M. Gunasekaran
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Sensor Node ◽  
Optimization Algorithms ◽  
Sensor Nodes ◽  
Hybrid Optimization ◽  
Wireless Sensor ◽  
Hybrid Optimization Algorithm ◽  
Localization Model ◽  
Anchor Nodes

The localization strategy is broadly utilized in Wireless Sensor Networks (WSNs) to detect the present location of the sensor nodes. A WSN comprises of multiple sensor nodes, which makes the employment of GPS on each sensor node costly, and GPS does not give accurate localization outcomes in an indoor environment. The process of configuring location reference on each sensor node manually is also not feasible in the case of a large dense network. Hence, this proposal plans to develop an intelligent model for developing localization pattern in WSN with a group of anchor nodes, rest nodes, and target nodes. The initial step of the proposed node localization model is the selection of the optimal location of anchor nodes towards the target nodes using the hybrid optimization algorithm by concerning the constraints like the distance between the nodes. The second step is to optimally determine the location of the rest node by reference to the anchor nodes using the same hybrid optimization algorithm. Here, the weight has to be determined for each anchor sensor node based on its Received Signal Strength (RSS), and RSS threshold value with the assistance of Neural Network. The hybrid optimization algorithms check the direction to where the concerned node has to be moved by merging the beneficial concepts of two renowned optimization algorithms named as Rider Optimization Algorithm (ROA), and Chicken Swarm Optimization Algorithm (CSO) to solve the localization problem in WSN. The newly developed hybrid algorithm is termed as Rooster Updated Attacker-based ROA (RUA-ROA). Finally, the comparative evaluation indicates a significant improvement in the proposed localization model by evaluating the convergence and statistical analysis.

Enhanced Inventory Management Using Blockchain Technology Under Cloud Sector Enabled by Hybrid Multi-Verse with Whale Optimization Algorithm

2021 ◽  
pp. 1-38
Author(s):  
Chinnaraj Govindasamy ◽  
Arokiasamy Antonidoss
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Optimization Algorithm ◽  
Transportation Cost ◽  
The Other ◽  
Hybrid Optimization ◽  
Setup Cost ◽  
Blockchain Technology ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.

scholarly journals MULTI OBJECTIVE AERODYNAMIC OPTIMIZATION USING PARALLEL NASH EVOLUTIONARY/DETERMINISTIC HYBRID ALGORITHMS

2016 ◽  
Vol 42 ◽  
pp. 1660168
Author(s):  
ZHILI TANG
Keyword(s):  
Drag Reduction ◽  
Optimization Algorithm ◽  
Aerodynamic Drag ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm ◽  
Aerodynamic Drag Reduction ◽  
Global Parameters

This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.

scholarly journals Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN

2021 ◽  
Vol 11 (4) ◽  
pp. 7436-7441
Author(s):  
N. K. Al-Shammari ◽  
A. A. Alzamil ◽  
M. Albadarn ◽  
S. A. Ahmed ◽  
M. B. Syed ◽  
...  
Keyword(s):  
Blood Flow ◽  
Optimization Algorithm ◽  
Missing Values ◽  
Search Algorithm ◽  
High Accuracy ◽  
Hybrid Optimization ◽  
Air Bubbles ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Detailed Evaluation

Heart weakness and restricted blood flow into the cavities can cause a range of strokes from mild to severe Heart strokes are primary caused due to the fat deposited on artery walls. The process reduces the intake of blood and internally causes a pseudo vacuum of air bubbles leading to a stroke which can be identified with high-end instrumentations. In this article, a detailed evaluation is processed with a Hybrid Optimization Algorithm (HOA). In the proposed technique, data are preprocessed using a label encoder and the missing values of the dataset are filled. Whale Optimization Algorithm (WOA) and Crow Search Algorithm(CSA) extract inter-connected patterns and learning features using a dedicated Deep Neural Networking (DNN) support. The proposed Hybrid Optimization Algorithm extracts features and the resultant values demonstrate a high accuracy range of 97.34%.

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