Research on Improved Chaotic Particle Optimization Algorithm Based on Complex Function

Xia, Xiangli; Li, Shijin

doi:10.3389/fphy.2020.00368

Cited by 8 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the complex function can be used in more basic academic tools. For example, The development of a chaotic particle optimization method for complicated functions aims to improve the efficiency of the Particle Swarm Optimization (PSO) algorithm in addressing continuous function optimization problems [10].…”

Section: Resultsmentioning

confidence: 99%

The Application of Complex Variables Function and Residue Theorem

Hu¹,

Lan²,

Lin³

et al. 2023

HSET

View full text Add to dashboard Cite

The history of complex function can be dated back to about 18 century when Euler came up with two equations derived from integrals of complex function. Over the age, mathematicians have been trying to explore and discuss more in this mysterious field, and this new branch gradually became prevalent in the 19 century mathematics like when calculus ruled the 18 century mathematics. In particular, the concept of residue was invented and became an important part in complex function, being applied in some special types of real integral. Also it is a generalization of Cauchy integral theorem and Cauchy integral formula. Nowadays, the area of complex function is filled with crystallization of the wisdom of countless scholars and researchers, and the precious mathematical treasure can also meet the needs of other academic areas like physics and biology. Summarizing the various approaches and examples of application in fields of mathematics and others can be a valuable topic. In this paper, the history of application and some examples for references of complex functions, and introduce the main concepts and formulas about them will be briefly discussed, including the area of complex numbers, analytic function, and residue. There are some relationships between some of the applications to be dig out and considerable real-world problems, and they are supposed to the generalization of those subjects can provide certain enlightenment to people interested in or during the study in fields of complex function.

show abstract

Section: Resultsmentioning

confidence: 99%

The Application of Complex Variables Function and Residue Theorem

Hu¹,

Lan²,

Lin³

et al. 2023

HSET

View full text Add to dashboard Cite

show abstract

“…where k refers to the number of current iteration; ω denotes the inertia weight; and c 2 denote the cognitive learning factor and social learning factor of the particle, respectively, which normally take values between

02

(Xia and Li, 2020 ), signifying the magnitude of the influence exerted by the experience of the particle itself and the population on the position movement of this particle; and r 1 and r 2 represent two numbers between [0, 1] that are generated randomly. It is precisely through the synergistic cooperation and information sharing among the particles that they decide the next movement (Shu et al, 2021 ).…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The IWCNS-PSO algorithm is refined by inertia weight cosine adjustment strategy, while the quality of the particles in the algorithm is optimized by the principle of natural selection. Through the fitness value and iteration time (Xia and Li, 2020 ), this paper assesses the merits and demerits of the algorithms, so as to embody the effectiveness of the proposed IWCNS- PSO algorithm in complex optimization problems, as well as the fact that the IWCNS-PSO algorithm features the best effect, which is relative to the other two PSO algorithms and the other two heuristic algorithms.…”

Section: Comparison and Verification Of Algorithm Performancementioning

confidence: 99%

Research on system of ultra-flat carrying robot based on improved PSO algorithm

Zhu,

Wu,

Chen

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method.

show abstract

“…Recently, several research studies have modified the original stochastic techniques by including chaos theory in their optimization processes, such as chaotic PSO [27], chaotic GSA [28], chaotic differential evolution [29], chaotic honey badger algorithm [30], chaotic ABC [31] and chaotic GA [32]. As a matter of fact, chaos systems are defined as deterministic nonlinear dynamic systems that are sensitive to initial conditions and their operating parameters [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile

et al. 2023

View full text Add to dashboard Cite

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

show abstract

Research on Improved Chaotic Particle Optimization Algorithm Based on Complex Function

Cited by 8 publications

References 13 publications

The Application of Complex Variables Function and Residue Theorem

The Application of Complex Variables Function and Residue Theorem

Research on system of ultra-flat carrying robot based on improved PSO algorithm

Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile

Contact Info

Product

Resources

About