scholarly journals Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6794
Author(s):  
Tomasz Krzeszowski ◽  
Krzysztof Wiktorowicz
Keyword(s):  
Discriminant Analysis ◽  
Swarm Intelligence ◽  
Hybrid Methods ◽  
Gait Recognition ◽  
Classification Methods ◽  
Grey Wolf ◽  
New Classification ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.

scholarly journals Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

2020 ◽  
Vol 16 (9) ◽  
pp. 155014772094913
Author(s):  
Mohamed Elhoseny ◽  
R Sundar Rajan ◽  
Mohammad Hammoudeh ◽  
K Shankar ◽  
Omar Aldabbas
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Swarm Intelligence ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

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 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 An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique

2021 ◽  
Vol 6 (3) ◽  
pp. 911-931
Author(s):  
Abhishek Sharma ◽  
Abhinav Sharma ◽  
Averbukh Moshe ◽  
Nikhil Raj ◽  
Rupendra Kumar Pachauri
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Parameter Extraction ◽  
Solar Pv ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Pv Cell ◽  
Gravitational Search

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

2021 ◽  
Vol 12 (2) ◽  
pp. 1-21
Author(s):  
Gokul Yenduri ◽  
Veeranjaneyulu Naralasetti
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Software Metrics ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Software Metric ◽  
New Variant ◽  
Conventional Models

Maintainability index (MI) is a software metric that offers measurements of the maintainability before release of the software by facilitating several substantial features of the system. In general, there is a common formula for determining the MI for all the software metrics to ensure the system's reliability. As it does not provide appropriate results regarding the reliability of the system, it is essential to focus on the next level of MI of software. Hence, this paper intends to allot an optimal weight and a constant to each software metric, which is optimized by grey wolf optimization (GWO). As a result, it can provide a new variant of MI by proposed enhanced model-GWO (EM-GWO). This optimized MI can ensure the efficiency of the respective software in such a way that it can provide an enhanced score from the system. Further, the proposed method is compared with conventional models such as enhanced model-generic algorithm (EM-GA), EM-particle swarm optimization (PSO), EM-ant bee colony (ABC), EM-differential evolution (DE), and EM-fire fly (FF), and the results are obtained.

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.

Sign in / Sign up

Export Citation Format

Share Document