Random Number Generator Based On Hybrid Algorithm Between Particle Swarm Optimization (PSO) Algorithm And 3D-Chaotic System And Its Application

The Soul is eternal and exists even after death of a person or animal. The main idea that is captured in this work is that soul continues to exist and takes a different a body after the death. The primary goal of this work is to invent a new field titled "Artificial Soul Optimization (ASO)". The term "Artificial Soul Optimization" is coined in this paper. All the Optimization algorithms which are proposed based on Artificial Souls will come under "Artificial Soul Optimization" Field (ASO Field). In the Particle Swarm Optimization and Artificial Human Optimization, the basic entities in search space are Artificial Birds and Artificial Humans respectively. Similarly, in Artificial Soul Optimization, the basic entities in search space are Artificial Souls. In this work, the ASO Field concepts are added to Particle Swarm Optimization (PSO) algorithm to create a new hybrid algorithm titled "Soul Particle Swarm Optimization (SoPSO). The proposed SoPSO algorithm is applied on various benchmark functions. Results obtained are compared with PSO algorithm. The World's first Hybrid PSO algorithm based on Artificial Souls is created in this work.

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Artificial God Optimization - A Creation

Computer and Information Science ◽

10.5539/cis.v13n1p41 ◽

2020 ◽

Vol 13 (1) ◽

pp. 41

Author(s):

Satish Gajawada ◽

Hassan M. H. Mustafa

Keyword(s):

Particle Swarm Optimization ◽

Hybrid Algorithm ◽

Optimization Problems ◽

Optimization Algorithms ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

The World ◽

Complex Optimization ◽

Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence, the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field, which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm, which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

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Artificial God Optimization – A Creation

Transactions on Machine Learning and Artificial Intelligence ◽

10.14738/tmlai.76.7343 ◽

2020 ◽

Vol 7 (6) ◽

pp. 01-10

Author(s):

Satish Gajawada ◽

Hassan Mustafa

Keyword(s):

Artificial Intelligence ◽

Particle Swarm Optimization ◽

Hybrid Algorithm ◽

Optimization Problems ◽

Optimization Algorithms ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Complex Optimization ◽

Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

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Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽

10.3390/s21082868 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2868

Author(s):

Gong Cheng ◽

Huangfu Wei

Keyword(s):

Particle Swarm Optimization ◽

Sensor Networks ◽

Particle Swarm ◽

Pso Algorithm ◽

The Internet ◽

Directional Sensor Networks ◽

Swarm Optimization ◽

Coverage Optimization ◽

Boundary Constraint ◽

Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201644 ◽

2021 ◽

pp. 1-17

Author(s):

J. Shobana ◽

M. Murali

Keyword(s):

Particle Swarm Optimization ◽

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

Contextual Information ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Complex & Intelligent Systems ◽

10.1007/s40747-020-00252-2 ◽

2021 ◽

Author(s):

Na Geng ◽

Zhiting Chen ◽

Quang A. Nguyen ◽

Dunwei Gong

Keyword(s):

Particle Swarm Optimization ◽

Task Allocation ◽

Optimal Algorithm ◽

Particle Swarm ◽

Evolutionary Process ◽

Pso Algorithm ◽

Swarm Optimization ◽

Fitness Evaluation ◽

Modified Particle Swarm Optimization ◽

Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

Remote Sensing ◽

10.3390/rs13061207 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1207

Author(s):

Junfei Yu ◽

Jingwen Li ◽

Bing Sun ◽

Yuming Jiang ◽

Liying Xu

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Geographic Location ◽

Doa Estimation ◽

Pso Algorithm ◽

Two Dimensional ◽

Swarm Optimization ◽

Location Method ◽

Esprit Algorithm ◽

To Receive

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

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Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

Applied Sciences ◽

10.3390/app11020839 ◽

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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