scholarly journals Feature selection algorithm for usability engineering: a nature inspired approach

2021 ◽  
Author(s):  
Rajat Jain ◽  
Tania Joseph ◽  
Anvita Saxena ◽  
Deepak Gupta ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Evaluation Method ◽  
Bat Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
User Friendliness ◽  
Software Usability ◽  
Software Model ◽  
Whale Optimization ◽  
Selection Of

AbstractSoftware usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

scholarly journals Improved grey wolf algorithm for optimization problems

2021 ◽  
Vol 22 (3) ◽  
pp. 1573
Author(s):  
Hafiz Maaz Asgher ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali ◽  
Muhammad Aamir
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Research Work ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Grey Wolf Optimization ◽  
Whale Optimization

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.

scholarly journals Ajuste Ótimo de ESP e SVC-POD para Amortecimento de Oscilações

2021 ◽  
Author(s):  
Wesley Peres ◽  
Bruna C. Ferreira ◽  
Fabrício C. Gonçalves ◽  
Felipe L. S. Magalhães ◽  
Junior N. N. Costa ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Power Oscillation ◽  
Whale Optimization ◽  
Gravitational Search

O amortecimento de oscilações de potência é essencial na operação de sistemas de potência. Oscilações não amortecidas ou fracamente amortecidas podem limitar a capacidade de transferência de potência e causar blecautes. Para resolver esse problema, estabilizadores de sistemas de potência (ESP) instalados em geradores síncronos têm sido utilizados desde a década de setenta. Outra opção é utilizar um controlador denominado Power Oscillation Damper (POD) em dispositivos FACTS tais como o Compensador Estático de Reativos (CER). Com o objetivo de melhorar a estabilidade dos sistemas de potência, um projeto ótimo e robusto de ESP e POD deve ser realizado. Considerado as soluções de boa qualidade fornecidas por metaheurísticas, esse artigo compara quatro técnicas (Whale Optimization Algorithm, Grey Wolf Optimization, Gravitational Search Algorithm e Algoritmos Genéticos) na solução do problema de otimização mencionado. O ajuste de controladores ESP e POD é formulado como um problema de otimização com o objetivo de maximizar o coeficiente de amortecimento do autovalor dominante em malha fechada considerando vários pontos de operação para garantia de robustez. Resultados para um sistema de duas áreas são discutidos.

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

scholarly journals Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

2021 ◽  
Vol 1821 (1) ◽  
pp. 012038
Author(s):  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hopfield Neural Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization
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