Swarm Intelligence in Solving Bio-Inspired Computing Problems

2015 ◽

pp. 74-98 ◽

Cited By ~ 6

Author(s):

Debi Prasanna Acharjya ◽

Ahmed P. Kauser

Keyword(s):

Swarm Intelligence ◽

Data Clustering ◽

Real Life ◽

Biological Data ◽

Behavioral Models ◽

Biologically Inspired ◽

Generative Power ◽

Life Problems ◽

Intelligence Technique

Currently, a huge amount of data is available across various domains including biological data. Classification of these data, clustering, and data analysis is tedious and has become popular in recent research. In particular, bio-inspired computing is the field that mends together mathematics, computer science, and biology to develop tools to store, scrutinize, and interpret the biological data. It is also used to solve real life problems like sequencing biological data, data clustering, and optimization. Swarm intelligence is an emerging field of biologically inspired artificial intelligence technique that is based on the behavioral models of social insects. This chapter provides an overview of swarm intelligence algorithms in solving bio-inspired computing problems. It is an attempt to explore the working nature, applications, and generative power of various bio-inspired computing algorithms. The main intent is to furnish a comprehensive study of swarm intelligence algorithms in the literature so as to inspire further research in the area of biologically inspired computing.

Download Full-text

Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Research Anthology on Clean Energy Management and Solutions ◽

10.4018/978-1-7998-9152-9.ch035 ◽

2021 ◽

pp. 843-868

Author(s):

Sunanda Hazra ◽

Provas Kumar Roy

Keyword(s):

Renewable Energy ◽

Swarm Intelligence ◽

Behavioral Models ◽

Biologically Inspired ◽

Practical Applications ◽

The Social ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization ◽

Emission Dispatch ◽

Swarm Intelligence Algorithm

Swarm intelligence is a promising field of biologically-inspired artificial intelligence, which is based on the behavioral models of social insects. This article covers Swarm Intelligence Algorithm, i.e., grasshopper optimization algorithm (GOA) which is based on the social communication nature of the grasshopper, applied to renewable energy based economic and emission dispatch problems. Based on Weibull probability density function (W-pdf), the stochastic wind speed including optimization problem is numerically solved for a 2 renewable wind energy incorporating 6 and 14 thermal units for 3 different loads. Moreover, to improve the solution superiority and convergence speed, quasi oppositional based learning (QOBL) is included with the main GOA algorithm. The performance of GOA and QOGOA is evaluated and the simulation results as well as statistical results obtained by these methods along with different other algorithms available in the literature are presented to demonstrate the validity and effectiveness of the proposed GOA and QOGOA schemes for practical applications.

Download Full-text

Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6574.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 8259-8265

Keyword(s):

Particle Swarm Optimization ◽

Swarm Intelligence ◽

Optimization Problems ◽

Real Life ◽

Particle Swarm ◽

Swarm Optimization ◽

Slow Convergence ◽

Inertia Weight ◽

Life Problems ◽

Fixed Constant

Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

Download Full-text

Classification of Sentiment Based on Movie Feedback Given By Audiences

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7237.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 2594-2602

Keyword(s):

Confusion Matrix ◽

Real Life ◽

Class Imbalance ◽

Extraction Methods ◽

Class Imbalance Problem ◽

Inverse Document Frequency ◽

Imbalance Problem ◽

Life Problems ◽

Document Frequency

The need for generating automated sentiment on audience feedbacks has been the need of the hour. Manually going through the entire movie feedback becomes tedious therefore an attempt to predict the polarity of a movie based on the reviews using machine learning models is done. Usage of the IMDB movie reviews dataset has been done for training and testing. In this study we also try to depict the real-life problems of class imbalance and train-test splits, hence obtaining solutions for the same. The problem of class imbalance in today’s world has affected a large amount of predictive applications such as cancer detection , fraudulent transactions in banks etc, hence this study is an attempt to perform a solution to solve the class imbalance problem. Use of the undersampling method has been done in this study to improve the accuracy of an imbalanced class. Feature extraction methods such as Bag of Words and Term Frequency Inverse document Frequency have been used to generate features from the reviews. The Logistic regression and SVM classifiers have been used in the study to measure the accuracy. Along with the accuracy the Confusion Matrix has also been calculated to showcase the class imbalance taking its effect on the accuracy.

Download Full-text

Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization

Axioms ◽

10.3390/axioms7030057 ◽

2018 ◽

Vol 7 (3) ◽

pp. 57 ◽

Cited By ~ 3

Author(s):

Qiaoyan Li ◽

Yingcang Ma ◽

Florentin Smarandache ◽

Shuangwu Zhu

Keyword(s):

Objective Function ◽

Data Clustering ◽

Clustering Algorithm ◽

Real Life ◽

Neutrosophic Set ◽

Entropy Maximization ◽

Fuzzy C Means ◽

Promising Tool ◽

Life Problems ◽

Benchmark Datasets

Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. The neutrosophic set, which is an extension of the fuzzy set, has received extensive attention in solving many real-life problems of inaccuracy, incompleteness, inconsistency and uncertainty. In this paper, we propose a new clustering algorithm, the single-valued neutrosophic clustering algorithm, which is inspired by fuzzy c-means, picture fuzzy clustering and the single-valued neutrosophic set. A novel suitable objective function, which is depicted as a constrained minimization problem based on a single-valued neutrosophic set, is built, and the Lagrange multiplier method is used to solve the objective function. We do several experiments with some benchmark datasets, and we also apply the method to image segmentation using the Lena image. The experimental results show that the given algorithm can be considered as a promising tool for data clustering and image processing.

Download Full-text

SELECTIVE VOTING — GETTING MORE FOR LESS IN SENSOR FUSION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001406004739 ◽

2006 ◽

Vol 20 (03) ◽

pp. 329-350 ◽

Cited By ~ 29

Author(s):

LIOR ROKACH ◽

ODED MAIMON ◽

REUVEN ARBEL

Keyword(s):

Human Resources ◽

Sensor Fusion ◽

Real Life ◽

Multiple Models ◽

Multiple Sensors ◽

Real Life Problem ◽

Life Problems ◽

Probabilistic Classification

Many real life problems are characterized by the structure of data derived from multiple sensors. The sensors may be independent, yet their information considers the same entities. Thus, there is a need to efficiently use the information rendered by numerous datasets emanating from different sensors. A novel methodology to deal with such problems is suggested in this work. Measures for evaluating probabilistic classification are used in a new efficient voting approach called "selective voting", which is designed to combine the classification of the models (sensor fusion). Using "selective voting", the number of sensors is decreased significantly while the performance of the integrated model's classification is increased. This method is compared to other methods designed for combining multiple models as well as demonstrated on a real-life problem from the field of human resources.

Download Full-text

Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3222-5.ch001 ◽

2020 ◽

pp. 1-26

Author(s):

Sunanda Hazra ◽

Provas Kumar Roy

Keyword(s):

Renewable Energy ◽

Swarm Intelligence ◽

Behavioral Models ◽

Biologically Inspired ◽

Practical Applications ◽

The Social ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization ◽

Emission Dispatch ◽

Swarm Intelligence Algorithm

Swarm intelligence is a promising field of biologically-inspired artificial intelligence, which is based on the behavioral models of social insects. This article covers Swarm Intelligence Algorithm, i.e., grasshopper optimization algorithm (GOA) which is based on the social communication nature of the grasshopper, applied to renewable energy based economic and emission dispatch problems. Based on Weibull probability density function (W-pdf), the stochastic wind speed including optimization problem is numerically solved for a 2 renewable wind energy incorporating 6 and 14 thermal units for 3 different loads. Moreover, to improve the solution superiority and convergence speed, quasi oppositional based learning (QOBL) is included with the main GOA algorithm. The performance of GOA and QOGOA is evaluated and the simulation results as well as statistical results obtained by these methods along with different other algorithms available in the literature are presented to demonstrate the validity and effectiveness of the proposed GOA and QOGOA schemes for practical applications.

Download Full-text