An efficient particle swarm optimization with homotopy strategy for global numerical optimization

2021 ◽  
pp. 1-15
Author(s):  
Zhaojun Zhang ◽  
Xuanyu Li ◽  
Shengyang Luan ◽  
Zhaoxiong Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Prior Knowledge ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Initial Population ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Homotopy Analysis

Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the particle swarm optimization algorithm, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework.

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.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

scholarly journals Improved Quantum Particle Swarm Optimization for Mangroves Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Zhehuang Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Stable Convergence ◽  
Network Parameter ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Quantum Particle Swarm Optimization

Quantum particle swarm optimization (QPSO) is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

Analysis of FBG Reflection Spectrum with PSO Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1953-1957
Author(s):  
Zhao Xia Wu ◽  
Shu Qiang Chen ◽  
Jun Wei Wang ◽  
Li Fu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
High Precision Measurement ◽  
Swarm Optimization ◽  
Fbg Sensor ◽  
Fast Convergence Rate ◽  
Force Sense

When the parameters were measured by using fiber Bragg grating (FBG) in practice, there were some parameters hard to measure, which would influenced the reflective spectral of FBG severely, and make the characteristic information harder to be extracted. Therefore, particle swarm optimization algorithm was proposed in analyzing the uniform force reflective spectral of FBG. Based on the uniform force sense theory of FBG and particle swarm optimization algorithm, the objective function were established, meanwhile the experiment and simulation were constructed. And the characteristic information in reflective spectrum of FBG was extracted. By using particle swarm optimization algorithm, experimental data showed that particle swarm optimization algorithm used in extracting the characteristic information not only was efficaciously and easily, but also had some advantages, such as high accuracy, stability and fast convergence rate. And it was useful in high precision measurement of FBG sensor.

scholarly journals An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Feng Qian ◽  
Mohammad Reza Mahmoudi ◽  
Hamïd Parvïn ◽  
Kim-Hung Pho ◽  
Bui Anh Tuan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Global Optimum ◽  
Initial Population ◽  
Swarm Optimization ◽  
Conventional Optimization

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

scholarly journals Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

2016 ◽  
Vol 11 (1) ◽  
pp. 58-67 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Initial Population ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

scholarly journals An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaofeng Lv ◽  
Deyun Zhou ◽  
Ling Ma ◽  
Yuyuan Zhang ◽  
Yongchuan Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis

The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

A New Improved Adaptive Hybrid Particle Swarm Optimization Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1710-1713
Author(s):  
Xiang Tian ◽  
Yue Lin Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Searching Ability ◽  
Learning Factors

This paper introduces the principles and characteristics of Particle Swarm Optimization algorithm, and aims at the shortcoming of PSO algorithm, which is easily plunging into the local minimum, then we proposes a new improved adaptive hybrid particle swarm optimization algorithm. It adopts dynamically changing inertia weight and variable learning factors, which is based on the mechanism of natural selection. The numerical results of classical functions illustrate that this hybrid algorithm improves global searching ability and the success rate.

scholarly journals Particle Swarm Optimization Algorithm for Leakage Power Reduction in VLSI Circuits

2016 ◽  
Vol 62 (2) ◽  
pp. 179-186 ◽  
Author(s):  
V. Leela Rani ◽  
M. Madhavi Latha
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Leakage Power ◽  
Vlsi Circuits ◽  
Verilog Hdl ◽  
Swarm Optimization ◽  
Technology Roadmap ◽  
Test Circuit

Abstract Leakage power is the dominant source of power dissipation in nanometer technology. As per the International Technology Roadmap for Semiconductors (ITRS) static power dominates dynamic power with the advancement in technology. One of the well-known techniques used for leakage reduction is Input Vector Control (IVC). Due to stacking effect in IVC, it gives less leakage for the Minimum Leakage Vector (MLV) applied at inputs of test circuit. This paper introduces Particle Swarm Optimization (PSO) algorithm to the field of VLSI to find minimum leakage vector. Another optimization algorithm called Genetic algorithm (GA) is also implemented to search MLV and compared with PSO in terms of number of iterations. The proposed approach is validated by simulating few test circuits. Both GA and PSO algorithms are implemented in Verilog HDL and the simulations are carried out using Xilinx 9.2i. From the simulation results it is found that PSO based approach is best in finding MLV compared to Genetic based implementation as PSO technique uses less runtime compared to GA. To the best of the author’s knowledge PSO algorithm is used in IVC technique to optimize power for the first time and it is quite successful in searching MLV.

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

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