scholarly journals Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

Electronics ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 19 ◽  
Author(s):  
Alejandro García-Monzó ◽  
Héctor Migallón ◽  
Antonio Jimeno-Morenilla ◽  
José-Luis Sánchez-Romero ◽  
Héctor Rico ◽  
...  
Keyword(s):  
Shared Memory ◽  
Heuristic Algorithms ◽  
Optimization Algorithms ◽  
Parallel Architectures ◽  
Computational Time ◽  
Critical Aspect ◽  
Parallel Performance ◽  
Teaching Learning Based Optimization ◽  
Time Required ◽  
Teaching Learning

A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms.

Comparative Evaluation of Algorithms for Achieving Ultrapacked Thermal Greases: Microstructural Models and Effective Behavior

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Sukshitha Achar P. L ◽  
Huanyu Liao ◽  
Ganesh Subbarayan
Keyword(s):  
Heuristic Algorithms ◽  
Volume Fraction ◽  
Augmented Lagrangian Method ◽  
Random Network ◽  
Optimization Algorithms ◽  
Particle Packing ◽  
Computational Time ◽  
Particle Volume ◽  
Volume Fractions ◽  
Number Of Particles

Abstract In this work, we develop and evaluate algorithms for generating ultrapacked microstructures of particles. Simulated microstructures reported in the literature rarely contain particle volume fractions greater than 60%. However, commercially available thermal greases appear to achieve volume fractions in the range of 60–80%. Therefore, to analyze the effectiveness of commercially available particle-filled thermal interface materials (TIM), there is a need to develop algorithms capable of generating ultrapacked microstructures. The particle packing problem is initially posed as a nonlinear programming problem, and formal optimization algorithms are applied to generate microstructures that are maximally packed. The packing efficiency in the simulated microstructure is dependent on the number of particles in the simulation cell; however, as the number of particles increases, the packing simulation is computationally expensive. Here, the computational time to generate microstructures with large number of particles is systematically evaluated first using optimization algorithms. The algorithms include the penalty function methods, best-in-class sequential quadratic programming method, matrix-less conjugate gradient method as well as the augmented Lagrangian method. Heuristic algorithms are next evaluated to achieve computationally efficient packing. The evaluated heuristic algorithms are mainly based on the drop-fall-shake (DFS) method, but modified to more effectively simulate the mixing process in commercial planetary mixers. With the developed procedures, representative volume elements (RVE) with volume fraction as high as 74% are demonstrated. The simulated microstructures are analyzed using our previously developed random network model to estimate the effective thermal and mechanical behavior given a particle arrangement.

scholarly journals Optimal Design of Passive and Active Control Systems in Seismic-excited Structures Using a New Modified TLBO

2020 ◽  
Author(s):  
Saeed Hosseinaei ◽  
Mohammad Reza Ghasemi ◽  
Sadegh Etedali
Keyword(s):  
Optimal Design ◽  
Structural Control ◽  
Passive Control ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Control Device ◽  
Feedback Gain ◽  
Active Structural Control ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Vibration control devices have recently been used in structures subjected to wind and earthquake excitations. The optimal design problems of the passive control device and the feedback gain matrix of the controller for the seismic-excited structures are some attractive problems for researches to develop optimization algorithms with the advancement in terms of simplicity, accuracy, speed, and efficacy. In this paper, a new modified teaching–learning-based optimization (TLBO) algorithm, known as MTLBO, is proposed for the problems. For some benchmark optimization functions and constrained engineering problems, the validity, efficacy, and reliability of the MTLBO are firstly assessed and compared to other optimization algorithms in the literature. The undertaken statistical indicate that the MTLBO performs better and reliable than some other algorithms studied here. The performance of the MTLBO will then be explored for two passive and active structural control problems. It is concluded that the MTLBO algorithm is capable of giving better results than conventional TLBO. Hence, its utilization as a simple, fast, and powerful optimization tool to solve particular engineering optimization problems is recommended.

Synthesis of Linear, Planar, and Concentric Circular Antenna Arrays Using Rao Algorithms

2020 ◽  
Vol 11 (3) ◽  
pp. 31-49
Author(s):  
Jaya Lakshmi Ravipudi
Keyword(s):  
Antenna Arrays ◽  
Optimization Algorithms ◽  
Side Lobe ◽  
Binary Particle Swarm Optimization ◽  
Lower Side ◽  
Improved Genetic Algorithm ◽  
Swarm Optimization ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Circular Antenna Arrays

The aim of this paper is to display the efficacy of three newly proposed optimization algorithms named as Rao-1, Rao-2, and Rao-3 in synthesizing antenna arrays. The algorithms are applied to three different antenna array configurations. Thinned arrays with isotropic radiators are considered and the main objective is to find the optimal configuration of ON/OFF elements that produce low side lobe levels. The results of Rao-1, Rao-2, and Rao-3 algorithms are compared with those of improved genetic algorithm (IGA), hybrid Taguchi binary particle swarm optimization (HTBPSO), teaching-learning-based optimization (TLBO), the firefly algorithm (FA), and biogeography-based optimization (BBO). The Rao-1, Rao-2, and Rao-3 algorithms were able to realize antenna arrays having lower side lobe levels (SLL) when compared to the other optimization algorithms.

Wrapper based Feature Selection using Integrative Teaching Learning Based Optimization Algorithm

2020 ◽  
Vol 17 (6) ◽  
pp. 885-894
Author(s):  
Mohan Allam ◽  
Nandhini Malaiyappan
Keyword(s):  
Feature Selection ◽  
Optimization Algorithms ◽  
Solution Space ◽  
Feature Space ◽  
Entire Solution ◽  
Training Dataset ◽  
Classification Models ◽  
Local Optima ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

The performance of the machine learning models mainly relies on the key features available in the training dataset. Feature selection is a significant job for pattern recognition for finding an important group of features to build classification models with a minimum number of features. Feature selection with optimization algorithms will improve the prediction rate of the classification models. But, tuning the controlling parameters of the optimization algorithms is a challenging task. In this paper, we present a wrapper-based model called Feature Selection with Integrative Teaching Learning Based Optimization (FS-ITLBO), which uses multiple teachers to select the optimal set of features from feature space. The goal of the proposed algorithm is to search the entire solution space without struck in the local optima of features. Moreover, the proposed method only utilizes teacher count parameter along with the size of the population and a number of iterations. Various classification models have been used for finding the fitness of instances in the population and to estimate the effectiveness of the proposed model. The robustness of the proposed algorithm has been assessed on Wisconsin Diagnostic Breast Cancer (WDBC) as well as Parkinson’s Disease datasets and compared with different wrapper-based feature selection techniques, including genetic algorithm and Binary Teaching Learning Based Optimization (BTLBO). The outcomes have confirmed that FS-ITLBO model produced the best accuracy with the optimal subset of features

scholarly journals Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangzhu He ◽  
Jida Huang ◽  
Yunqing Rao ◽  
Liang Gao
Keyword(s):  
Numerical Optimization ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Lévy Flight ◽  
Global Numerical Optimization ◽  
Levy Flight ◽  
Local Optima ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Different Characteristics

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

Meta-heuristic optimization algorithms for synthesis of reconfigurable hexagonal array antenna in two principle vertical planes

2021 ◽  
pp. 1-8
Author(s):  
Bitan Misra ◽  
Gautam Kumar Mahanti
Keyword(s):  
Optimization Algorithms ◽  
Amplitude Distribution ◽  
Excitation Amplitude ◽  
Heuristic Optimization ◽  
Simulation Based ◽  
Optimal Current ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Symbiotic Organism Search ◽  
Flat Top Beam

Abstract This study illustrates the dynamical reconfiguration of a concentric hexagonal antenna array radiation to generate a pencil beam and flat-top beam simultaneously by electronic control in two principle vertical planes under consideration. Both the beams share a common normalized optimal current excitation amplitude distribution while the optimal sets of phase excitation coefficients are varied radically across the hexagons to generate a flat-top beam. The proposed approach is able to solve the underlying multi-objective problem and flexible enough to the efficient implementation of additional design constraints in the considered φ-planes. In this paper, a set of simulation-based examples are presented in an integrated way. The outcomes validate the effectiveness of the stated optimization using meta-heuristic optimization algorithms (teaching–learning-based optimization, symbiotic organism search, multi-verse optimization) to reach the solution globally and prove actual relevance to the concerned applications.

scholarly journals MLO: Multi Leader Optimizer

2020 ◽  
Vol 13 (6) ◽  
pp. 364-373
Author(s):  
Mohammad Dehghani ◽  
◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Ricardo Ramirez-Mendoza ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Emperor Penguin ◽  
Gray Wolf ◽  
Test Functions ◽  
Gray Wolf Optimizer ◽  
Grasshopper Optimization Algorithm ◽  
Teaching Learning Based Optimization ◽  
Grasshopper Optimization ◽  
Teaching Learning

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

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