A NOVEL OPTIMIZATION ALGORITHM BASED ON ATMOSPHERE CLOUDS MODEL

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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Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2019040102 ◽

2019 ◽

Vol 13 (2) ◽

pp. 18-29

Author(s):

Rongrong Li ◽

Linrun Qiu ◽

Dongbo Zhang

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Bottom Layer ◽

Pso Algorithm ◽

Particle Swarm Algorithm ◽

Swarm Optimization ◽

Elite Group ◽

Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

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Tasks mapping in the network on a chip using an improved optimization algorithm

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-07-2019-0053 ◽

2020 ◽

Vol 16 (2) ◽

pp. 165-182

Author(s):

Mehdi Darbandi ◽

Amir Reza Ramtin ◽

Omid Khold Sharafi

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Heuristic Algorithms ◽

Particle Swarm ◽

Pso Algorithm ◽

Computational Time ◽

Swarm Optimization ◽

Content Type ◽

Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

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Vehicle Path Planning Method Based on Particle Swarm Optimization Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.2745 ◽

2012 ◽

Vol 468-471 ◽

pp. 2745-2748

Author(s):

Sheng Long Yu ◽

Yu Ming Bo ◽

Zhi Min Chen ◽

Kai Zhu

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Path Planning ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Adaptive Genetic Algorithm ◽

Swarm Optimization ◽

Vehicle Path

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. Particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. Particle swarm optimization algorithm a kind of swarm-based optimization method.The simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

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An enhanced particle swarm optimization algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp4904-4907 ◽

2019 ◽

Vol 9 (6) ◽

pp. 4904

Author(s):

Wameedh Riyadh Abdul-Adheem

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Test Results ◽

Swarm Optimization ◽

The Social ◽

Basic Particle ◽

Stochastic Optimization Algorithm ◽

Enhanced Particle Swarm Optimization

<p>In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.</p>

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Antenna optimization using Particle Swarm Optimization algorithm

Journal of Automatic Control ◽

10.2298/jac0601021g ◽

2006 ◽

Vol 16 (1) ◽

pp. 21-24 ◽

Cited By ~ 11

Author(s):

Ruzica Golubovic ◽

Dragan Olcan

Keyword(s):

Particle Swarm Optimization ◽

Antenna Array ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Yagi Antenna ◽

Forward Gain

We present the results for two different antenna optimization problems that are found using the Particle Swarm Optimization (PSO) algorithm. The first problem is finding the maximal forward gain of a Yagi antenna. The second problem is finding the optimal feeding of a broadside antenna array. The optimization problems have 6 and 20 optimization variables, respectively. The preferred values of the parameters of the PSO algorithm are found for presented problems. The results show that the preferred parameters of PSO are somewhat different for optimization problems with different number of dimensions of the optimization space. The results that are found using the PSO algorithm are compared with the results that are found using other optimization algorithms, in order to estimate the efficiency of the PSO.

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Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms ◽

10.4018/978-1-7998-8048-6.ch010 ◽

2021 ◽

pp. 190-204

Author(s):

Rongrong Li ◽

Linrun Qiu ◽

Dongbo Zhang

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Bottom Layer ◽

Pso Algorithm ◽

Particle Swarm Algorithm ◽

Swarm Optimization ◽

Elite Group ◽

Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

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A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

Advances in Mechanical Engineering ◽

10.1177/1687814018824930 ◽

2019 ◽

Vol 11 (3) ◽

pp. 168781401882493 ◽

Cited By ~ 1

Author(s):

Hao Zhu ◽

Yumei Hu ◽

Weidong Zhu

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Convergence Rate ◽

Engineering Design ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Particle Swarm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

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A Chaotic Hybrid Butterfly Optimization Algorithm with Particle Swarm Optimization for High-Dimensional Optimization Problems

Symmetry ◽

10.3390/sym12111800 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1800

Author(s):

Mengjian Zhang ◽

Daoyin Long ◽

Tao Qin ◽

Jing Yang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Numerical Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

One Dimensional ◽

Comparison Results ◽

Dimensional Optimization

In order to solve the problem that the butterfly optimization algorithm (BOA) is prone to low accuracy and slow convergence, the trend of study is to hybridize two or more algorithms to obtain a superior solution in the field of optimization problems. A novel hybrid algorithm is proposed, namely HPSOBOA, and three methods are introduced to improve the basic BOA. Therefore, the initialization of BOA using a cubic one-dimensional map is introduced, and a nonlinear parameter control strategy is also performed. In addition, the particle swarm optimization (PSO) algorithm is hybridized with BOA in order to improve the basic BOA for global optimization. There are two experiments (including 26 well-known benchmark functions) that were conducted to verify the effectiveness of the proposed algorithm. The comparison results of experiments show that the hybrid HPSOBOA converges quickly and has better stability in numerical optimization problems with a high dimension compared with the PSO, BOA, and other kinds of well-known swarm optimization algorithms.

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HYBRID IMPROVED BACTERIAL SWARM OPTIMIZATION ALGORITHM FOR HAND-BASED MULTIMODAL BIOMETRIC AUTHENTICATION SYSTEM

Journal of Information and Communication Technology ◽

10.32890/jict2019.18.2.8284 ◽

2019 ◽

Author(s):

Karthikeyan Shanmugasundaram ◽

Ahmad Sufril Azlan Mohmed ◽

Nur Intan Raihana Ruhaiyem

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Pso Algorithm ◽

Premature Convergence ◽

Biometric Authentication ◽

Step Size ◽

Bacterial Foraging Optimization ◽

Swarm Optimization ◽

Authentication System

This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.

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