Particle swarm optimization performance improvement using deep learning techniques

Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature. 

An enhanced black widow optimization algorithm for feature selection

Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature.

Reconstruction, optimization, and design of heterogeneous materials and media: Basic principles, computational algorithms, and applications

Power system applications often require solving one or multiple optimization problems which are nonlinear. Due to the limitations such as dimensionality constraints and slow convergence as offered by the analytical methods, swarm intelligence-based methods have emerged as a practical optimization problem solution alternate. Particle swarm optimization (PSO), a well-known member of the swarm intelligence-based family, has been acknowledged as an efficient solution for nonlinear extensive optimization problems. On the one hand, this article provides insights on basic notions and progress of the PSO in power system applications and case studies highlighting the best performance of PSO and its variants. Technical visions essential for the application of PSO, like its form, particle presentation, and the most competent fitness functions, are also depicted for the respective application. On the other hand, it presents a comprehensive bibliographic analysis of 144 high-impact journal articles concerning the prominent applications of PSO as an optimization method in power systems, having more than twenty citations and ordered by the number of citations in descending order. The outcomes of this bibliographic scrutiny demonstrate that Elsevier, IEEE, and Springer articles are the most significant. It also recognizes the most influential and high-impact journals and countries/regions.

Advances and bibliographic analysis of particle swarm optimization applications in electrical power system: concepts and variants

The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.

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Particle Swarm Optimisation: A Historical Review Up to the Current Developments

Finding arbitrary roots of polynomials is a fundamental problem in various areas of science and engineering A myriad of methods was suggested to address this problem, such as the sequential Newton’s method and the Durand–Kerner (D–K) simultaneous iterative method The sequential iterative methods, on the one hand, need to use a deflation procedure in order to compute approximations to all the roots of a given polynomial, which can produce inaccurate results due to the accumulation of rounding errors On the other hand, the simultaneous iterative methods require good initial guesses to converge However, Artificial Neural Networks (ANNs) are widely known by their capacity to find complex mappings between the dependent and independent variables In view of this, this paper aims to determine, based on comparative results, whether ANNs can be used to compute approximations to the real and complex roots of a given polynomial, as an alternative to simultaneous iterative algorithms like the D–K method Although the results are very encouraging and demonstrate the viability and potentiality of the suggested approach, the ANNs were not able to surpass the accuracy of the D–K method The results indicated, however, that the use of the approximations computed by the ANNs as the initial guesses for the D–K method can be beneficial to the accuracy of this method

/pdf/a-neural-network-based-approach-for-approximating-arbitrary-am4rhf8an9.pdf

A Neural Network-Based Approach for Approximating Arbitrary Roots of Polynomials

A substantial part of rehabilitation tools are developed in the context of research projects, which rarely reach their target audience, in particular clinicians and patients. To address this challenge, the Open Rehab Initiative (ORI) emerged as an international independent online portal with the goal of connecting clinicians, scientists, engineers, game developers, and end-users to interact and share virtual rehabilitation tools. The quality and efficacy of such platform can only be attained iteratively based on a user-centered design approach. This paper describes the design process and features implemented on the second development iteration subsequent to a formative evaluation of the first version of ORI. The main goal of the second iteration was to implement new features and make the platform functional and ready for a second evaluation process with beta testers, which will certainly prompt new features for improvement and will serve as a step toward the final release of the site.

Diogo Freitas

Papers

Particle Swarm Optimisation: A Historical Review Up to the Current Developments

A Neural Network-Based Approach for Approximating Arbitrary Roots of Polynomials

Open Rehab Initiative: Second development iteration