DISCRETE OPTIMIZATION PROBLEMS OF LINEAR ARRAY SYNTHESIS BY USING REAL NUMBER PARTICLE SWARM OPTIMIZATION

AbstractMeta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain “easy” reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.

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Chunking and cooperation in particle swarm optimization for feature selection

Annals of Mathematics and Artificial Intelligence ◽

10.1007/s10472-021-09752-4 ◽

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.

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A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽

10.3390/en14154613 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4613

Author(s):

Shah Fahad ◽

Shiyou Yang ◽

Rehan Ali Khan ◽

Shafiullah Khan ◽

Shoaib Ahmed Khan

Keyword(s):

Particle Swarm Optimization ◽

Optimization Problems ◽

Quantum Particle ◽

Particle Swarm ◽

Continuous Variables ◽

Design Problems ◽

Swarm Optimization ◽

Electromagnetic Design ◽

Quantum Particle Swarm Optimization ◽

Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

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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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Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽

10.1190/geo2019-0111.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. R767-R781 ◽

Cited By ~ 1

Author(s):

Mattia Aleardi ◽

Silvio Pierini ◽

Angelo Sajeva

Keyword(s):

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Model Space ◽

Global Search ◽

Objective Functions ◽

Swarm Optimization ◽

Fireworks Algorithm ◽

Highly Nonlinear ◽

Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

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