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%.

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.

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.

A Novel Hybrid Optimization Algorithm of Pattern Search and IWO

2014 ◽  
Vol 687-691 ◽  
pp. 1557-1559
Author(s):  
Hui Hui Xiao
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Pattern Search ◽  
Hybrid Optimization ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Intelligent Optimization ◽  
Hybrid Optimization Algorithm ◽  
Simulation Results ◽  
Swarm Intelligence Algorithm

Invasive weed optimization algorithm is a new swarm intelligence algorithm recently. The algorithm has better robustness and adaptation, which is a very good intelligent optimization tools; but it is easy to fall into local optimization, and having the low speed of convergence, and it can not acquire exactly. Aiming at the shortcomings of the algorithm, taking advantage of pattern search excellent local search ability, this paper presents a novel hybrid optimization algorithm of pattern search algorithm and IWO optimization. The Simulation results of three standard benchmark functions show that the improved algorithm can greatly improve the convergence precision and convergence speed, and can effectively discourage the premature convergence.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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