Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization

Lin, Guo; Zhang, Jing; Liu, Zhaohua

doi:10.1007/s11633-016-0990-6

Cited by 52 publications

(17 citation statements)

References 25 publications

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“…(2) Collect the input and output data u t and y t , t = 1, 2, … , p, form φ 1,k t by (27) and φ k t by (26). Construct Y p and Φ k p by (31) and (32), respectively.…”

Section: The Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…This algorithm has attracted the attention of academia with the advantages of easy implementation, high precision, and fast convergence [26]. Compared with the conventional optimization methods, it has excellent optimized performances and characteristics [27]. The particle swarm optimization algorithm has been widely used in function optimization, system identification, and fuzzy control [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise

Xiao

2018

Complexity

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This paper considers the parameter identification of Wiener systems with colored noise. The difficulty in the identification is that the model is nonlinear and the intermediate variable cannot be measured. Particle swarm optimization is an artificial intelligence evolutionary method and is effective in solving nonlinear optimization problem. In this paper, we obtain the identification model of the Wiener system and then transfer the parameter identification problem into an optimization problem. Then, we derive a particle swarm optimization iterative (PSOI) identification algorithm to identify the unknown parameter of the Wiener system. Furthermore, a gradient iterative identification algorithm is proposed to compare with the particle swarm optimization iterative algorithm. Numerical simulation is carried out to evaluate the performance of the PSOI algorithm and the gradient iterative algorithm. The simulation results indicate that the proposed algorithms are effective and the PSOI algorithm can achieve better performance over the gradient iterative algorithm.

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“…(2) Collect the input and output data u t and y t , t = 1, 2, … , p, form φ 1,k t by (27) and φ k t by (26). Construct Y p and Φ k p by (31) and (32), respectively.…”

Section: The Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise

Xiao

2018

Complexity

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“…Hybridization of DE with other algorithms is another way to overcome the drawbacks of both algorithms and further enhance the optimization performance. Depending on the type of algorithm, the DE can be hybridized with other EAs, such as ABC, CS, and PSO [13,22] or with different local search methods such as Powell's method, the Hook-Jeeves (HJ), and the Nelder-Mead (NM) [23][24][25]. Among them, the NM method has been chosen, due to its excellent local search ability.…”

Section: Introductionmentioning

confidence: 99%

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020

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This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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“…The consensus has developed rapidly and yielded fruitful results, and has been widely applied to a variety of scientific and engineering problems, including synchronization of coupled oscillators, formation control, swarm control, optimal cooperative control, clustering, sensor networks, etc. (Lin, Zhang, & Liu, 2018;Zhang, Hu, Liu, Yu, & Liu, 2019). This paper will introduce mainly the development and research status from the following aspects: consensus subjected to communication constraints, leaderfollowing consensus, group consensus, consensus based on trigger mechanism, finite-time consensus, multiple consensus and multiple tracking.…”

Section: Introductionmentioning

confidence: 99%

A survey of the consensus for multi-agent systems

Tan

2019

Systems Science & Control Engineering

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Multi-agent systems (MASs) has developed into an emerging complex system science and gradually infiltrated into various fields of social life. The problem of consensus (i.e. all agents eventually to reach an agreement upon a common quantity of interest) is the basis of distributed coordinated control of the MASs, which has attracted tremendous attention from both theoretical and practical perspectives. This paper comprehensively reviews the state-of-the-art development in the consensus of MASs. Firstly, the basic framework and overview of MASs and consensus are discussed. Secondly, the motivations, results and methods of several kinds of consensus problems are introduced, including consensus subjected to communication constraints, leader-following consensus, group consensus, consensus based on trigger mechanism, finite-time consensus, multi-consensus and multi-tracking. Finally, some challenging issues and development trends of the consensus of MASs are considered.

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Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization

Cited by 52 publications

References 25 publications

Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise

Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

A survey of the consensus for multi-agent systems

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