Conceptual and numerical comparisons of swarm intelligence optimization algorithms

Ma, Haiping; Ye, Sengang; Simon, Dan; Fei, Minrui

doi:10.1007/s00500-015-1993-x

Cited by 31 publications

(16 citation statements)

References 49 publications

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“…Compared with other swarm intelligence (SI) optimization methods such as ant colony optimization (ACO) [30], shuffled frog leaping algorithm (SFLA) [31], and artificial bee colony (ABC) algorithm [32], the underlying idea behind PSO is similar to other SI algorithms [33]. However, PSO has easier implementability and lower computational complexity.…”

Section: Posture Optimization Algorithm Based On the Skin Modelmentioning

confidence: 99%

Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

Zhang

Sherar

et al. 2018

Mathematical Problems in Engineering

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Geometric deviations inevitably occur in product manufacturing and seriously affect the assembly quality and product functionality. Assembly simulations on the basis of computer-aided design (CAD) package could imitate the assembly process and thus find out the design deficiencies and detect the assemblability of the components. Although lots of researches have been done on the prediction of assembly variation considering the geometric errors, most of them only simplify the geometric variation as orientation and position deviation rather than the manufacturing deformation. However, in machinery manufacturing, even if the manufacturing defects are limited, they could propagate and accumulate through components and lead to a noncompliant assembly. Recently, many point-based models have been applied to assembly simulation; however they are mainly interested in simulating the resulting positions of the assembled parts and lack the consideration of the postprocessing after positioning. This paper enriches the complete assembly simulation process based on skin model and presents a simple and effective posture evaluation and optimization method. The studied approach includes a software algorithm applied to evaluate the contact state of the assembly parts and a mathematical model based on the particle swarm optimization to acquire the optimal assembly posture. To verify the efficiency and feasibility of the proposed method, a case study on the aircraft wing box scaling model assembly is performed.

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Section: Posture Optimization Algorithm Based On the Skin Modelmentioning

confidence: 99%

Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

Zhang

Sherar

et al. 2018

Mathematical Problems in Engineering

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“…Another notable optimization algorithm currently being employed is the artificial bee colony, and the main reason of using this algorithm is due to its triple search capability which forages the local and global search space for the optimum solution. The comparative investigations of ABC optimization technique are carried out against GA, ACO and PSO, ABC searching is able to find a better optimum solution [24], the similar performance is exhibited by the PSO technique as well [24][25][26]. However, ABC algorithm possesses a higher speed in comparison with PSO [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…The comparative investigations of ABC optimization technique are carried out against GA, ACO and PSO, ABC searching is able to find a better optimum solution [24], the similar performance is exhibited by the PSO technique as well [24][25][26]. However, ABC algorithm possesses a higher speed in comparison with PSO [25,26]. For improving the performance of the fuzzy logic controller, enhancing the robustness of the controller and diminishing human intervention in the operation process, some control approaches were proposed by combining fuzzy logic control and artificial bee colony (ABC) algorithm [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

Bai¹,

Yang²,

Li³

et al. 2019

J. vibroeng.

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Combining P-type iterative learning (IL) control, fuzzy logic control and artificial bee colony (ABC) algorithm, a new optimal fuzzy IL controller is designed for active vibration control of piezoelectric smart structures. In order to accelerate the learning speed of feedback gain, the fuzzy logic controller is integrated into the ANSYS finite element (FE) models by using APDL (ANSYS Parameter Design Language) approach to adjust adaptively the learning gain of P-type IL control. For improving the performance and robustness of the fuzzy logic controller as well as diminishing human intervention in the operation process, ABC algorithm is used to automatically identify the optimal configurations for values in fuzzy query table, fuzzification parameters and defuzzification parameters, and the main program of ABC algorithm is operated in MATLAB. The active vibration equations are driven from the FE equations for the dynamic response of a linear elastic piezoelectric smart structure. Considering the vibrations generated by various external disturbances, the optimal fuzzy IL controller is numerically investigated for a clamped piezoelectric smart plate. Results demonstrate that the proposed control approach makes the feedback gain has a fast learning speed and performs excellent in vibration suppression. This is demonstrated in the results by comparing the new control approach with the P-type IL control.

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“…Nature-inspired optimization algorithms have become increasingly common in solving optimization problems in recent years [44]. ese algorithms include two types of methods: evolutionary algorithms (EAs) [45] and swarm intelligence (SI) algorithms [46]. EAs are inspired by the principles of population genetics and natural selection.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of Synchronous Reluctance Motors Using the Discrete Sliding Mode Control Technique

Rahman

Zribi

Smaoui

2019

Mathematical Problems in Engineering

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The synchronous reluctance motor (SynRM) drive system is known to exhibit chaotic behavior under specified conditions. In this paper, the discrete-time sliding mode control (DSMC) technique is used to synchronize two SynRMs starting from different sets of initial conditions. The mixed variable speed reaching law is adopted in the design of the controller scheme. The parameters of the designed control scheme are tuned using a genetic algorithm (GA). Simulation results are presented to demonstrate the effectiveness of the proposed controller. In addition, the performance of the proposed control scheme is studied through simulations when bounded disturbances and mismatches between the parameters of the systems and those of the control scheme exist. The simulation results show that the designed control scheme is robust to bounded external disturbances and to mismatches in the parameters of the systems.

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Conceptual and numerical comparisons of swarm intelligence optimization algorithms

Cited by 31 publications

References 49 publications

Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

Synchronization of Synchronous Reluctance Motors Using the Discrete Sliding Mode Control Technique

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