A New Chaos Optimization Algorithm and its Application

2010 ◽  
Vol 439-440 ◽  
pp. 594-598
Author(s):  
Li Fen Lu ◽  
Chun Bo Xiu
Keyword(s):  
Optimization Algorithm ◽  
Small Probability ◽  
Optimal Point ◽  
Chaos Optimization ◽  
Large Probability ◽  
Chaos Optimization Algorithm ◽  
Global Optimal ◽  
Simulation Results

In order to avoid blind searching before reducing the searching space of optimized variable and enhance searching efficiency in chaos optimization algorithm, a new mutative scale chaos optimization algorithm, Probability Chaos Optimization Algorithm (PCOA) was proposed. The current searching space is searched according to large probability and the origin space is searched according to small probability. Though the searching space is shrunk prematurely, the global optimal point can be found because the origin space is still searched according to small probability, which can overcome the shortcoming of losing the global optimal points owing to prematurely shrinking the searching space of the optimized variables in conventional mutative scale chaos optimization algorithm. The simulation results prove the validity of the algorithm.

Mutative Scale Chaos Optimization Algorithm and its Application in PID Controller Design of Mobile Robot Systems

2011 ◽  
Vol 179-180 ◽  
pp. 983-988
Author(s):  
Wen Bai Chen ◽  
Ran Gu ◽  
Jin Ao Li ◽  
Yu Mei Lu
Keyword(s):  
Mobile Robot ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Optimal Parameter ◽  
Parameters Optimization ◽  
Performance Criteria ◽  
Optimization Strategy ◽  
Optimal Point ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm

Ergodicity is the outstanding character of chaos system, and chaotic variables can be used to approach global optimal point. Aiming at the control requirements for position and speed of mobile robot, a compact PID controller parameters optimization strategy based on mutative scale chaos optimization algorithm is proposed. The Kinematics model of the soccer robot system is introduced, and the strategy of chaos optimization PID controller parameters is discussed. This optimal solution needs no prior knowledge, searches the whole parameter’s space efficiently and finds the optimal parameter, according to the given performance criteria. Simulation results show that this strategy is feasible and effective.

Calibration method of PCMA by using niching chaos optimization algorithm

2018 ◽  
Vol 26 (8) ◽  
pp. 2048-2056
Author(s):  
林苍现 RIM Chang-Hyon ◽  
林哲民 RIM Chol-Min ◽  
陈 刚 CHEN Gang ◽  
李评哲 RI Pyong-Chol
Keyword(s):  
Optimization Algorithm ◽  
Calibration Method ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm

Application of Parallel Chaos Optimization Algorithm for Plug-in Hybrid Electric Vehicle Design

2014 ◽  
Vol 24 (01) ◽  
pp. 1450001 ◽  
Author(s):  
Xiaolan Wu ◽  
Guifang Guo ◽  
Jun Xu ◽  
Binggang Cao
Keyword(s):  
Optimization Algorithm ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Optimization Problem ◽  
Optimization Method ◽  
Vehicle Performance ◽  
Chaos Optimization ◽  
Performance Requirements ◽  
Chaos Optimization Algorithm ◽  
Hybrid Electric

Plug-in hybrid electric vehicles (PHEVs) have been offered as alternatives that could greatly reduce fuel consumption relative to conventional vehicles. A successful PHEV design requires not only optimal component sizes but also proper control strategy. In this paper, a global optimization method, called parallel chaos optimization algorithm (PCOA), is used to optimize simultaneously the PHEV component sizes and control strategy. In order to minimize the cost, energy consumption (EC), and emissions, a multiobjective nonlinear optimization problem is formulated and recast as a single objective optimization problem by weighted aggregation. The driving performance requirements of the PHEV are considered as the constraints. In addition, to evaluate the objective function, the optimization process is performed over three typical driving cycles including Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), and New European Driving Cycle (NEDC). The simulation results show the effectiveness of the proposed approach for reducing the fuel cost, EC and emissions while ensuring that the vehicle performance has not been sacrificed.

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