An Efficient Chaotic Salp Swarm Optimization Approach Based on Ensemble Algorithm for Solving Class Imbalance Problem

2021 ◽  
Author(s):  
Rekha G ◽  
Krishna Reddy V ◽  
chandrashekar jatoth ◽  
Ugo Fiore
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Class Imbalance ◽  
Optimization Approach ◽  
Class Imbalance Problem ◽  
Swarm Optimization ◽  
Adaboost Algorithm ◽  
Wide Range

Abstract Class imbalance problems have attracted the research community but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an Adaboost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization and Adaboost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of Adaboost.

scholarly journals Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Aram M. Ahmed ◽  
Tarik A. Rashid ◽  
Soran Ab. M. Saeed
Keyword(s):  
Performance Evaluation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Approach ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Cat Swarm Optimization ◽  
Statistical Approaches ◽  
And Performance

This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

scholarly journals Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

2014 ◽  
Vol 4 (3) ◽  
pp. 189-204 ◽  
Author(s):  
Simone A. Ludwig
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Main Idea ◽  
Benchmark Problems ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Wide Range ◽  
Adaptive Pso ◽  
Self Adaptive

Abstract Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.

scholarly journals Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

2020 ◽  
Author(s):  
Aram M. Ahmed ◽  
Tarik A. Rashid ◽  
Soran AM. Saeed
Keyword(s):  
Performance Evaluation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Approach ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Cat Swarm Optimization ◽  
Statistical Approaches ◽  
And Performance

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

scholarly journals Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

2020 ◽  
Author(s):  
Aram M. Ahmed ◽  
Tarik A. Rashid ◽  
Soran AM. Saeed
Keyword(s):  
Performance Evaluation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Approach ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Cat Swarm Optimization ◽  
Statistical Approaches ◽  
And Performance

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

scholarly journals A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Alexandre Szabo ◽  
Leandro Nunes de Castro
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Swarm Algorithms ◽  
Wide Range

The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass), is a derivation of a particle swarm clustering algorithm and its architecture, as in most classifiers, is pre-defined. The second proposal, named Constructive Particle Swarm Classifier (cPSClass), uses ideas from the immune system to automatically build the swarm. A sensitivity analysis of the growing procedure of cPSClass and an investigation into a proposed pruning procedure for this algorithm are performed. The proposals were applied to a wide range of databases from the literature and the results show that they are competitive in relation to other approaches, with the advantage of having a dynamically constructed architecture.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

2021 ◽  
Author(s):  
Sayan Surya Shaw ◽  
Shameem Ahmed ◽  
Samir Malakar ◽  
Laura Garcia-Hernandez ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Class Imbalance ◽  
Ring Theory ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Swarm Optimization ◽  
Imbalance Problem ◽  
Representative Samples ◽  
Selection Of

AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

scholarly journals Chunking and cooperation in particle swarm optimization for feature selection

2021 ◽  
Author(s):  
Malek Sarhani ◽  
Stefan Voß
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Cooperative Learning ◽  
Optimization Problems ◽  
Practical Importance ◽  
Particle Swarm ◽  
Large Datasets ◽  
Social Phenomena ◽  
Swarm Optimization ◽  
Complex Optimization

AbstractBio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

scholarly journals Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 839
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Xiaotong Cui ◽  
Liang Dong ◽  
Muhammad Saad Khan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Optimization Problems ◽  
Classical Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electromagnetic Analysis ◽  
Swarm Optimization ◽  
Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

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