Research On Optimization And Control Methods For Complex Dynamic System

Complex dynamic systems are currently a research focus in the field of systems engineering.These systems exhibit characteristics such as high coupling,emergence,instability,nonlinearity,uncertainty,and unpredictability,making optimizing and controlling these systems a global challenge.Complex dynamic systems may experience high failure rates and large energy consumption,leading to economic losses and even jeopardizing personal safety.To address these issues,this article researches critical issues such as optimization,regulation,and optimal control of complex dynamic systems and combines meta-heuristic algorithms to form a complete set of optimization and regulation analysis methods for complex dynamic systems.These methods have significant theoretical implications.Meanwhile,taking the mine ventilation system as an example,the proposed methods are validated in practical engineering applications and have been recognized by experts at home and abroad.First,a single-objective optimization method for complex dynamic systems based on an improved Equilibrium optimizer algorithm is proposed.The method uses chaotic mapping for population initialization,improving the algorithm’s global search capability;a trigonometric function adjustment strategy improves the index,enhancing local search capability;Gaussian perturbation is introduced to increase particle diversity,avoiding local optima;finally,a learning factor is introduced to accelerate convergence speed and improve algorithm integrity.Benchmark test functions are used to verify the algorithm’s performance.The actual engineering application demonstrates that the method significantly reduces energy consumption in mine ventilation systems,saving about 2 million RMB per year,thus verifying the method’s effectiveness.Second,we find that single-objective optimization methods cannot meet the multi-objective optimization needs of complex dynamic systems.Therefore,a hybrid multi-objective equilibrium optimization algorithm based on the R2 index is further proposed.The algorithm uses the R2 index to improve Pareto solution ranking rules,improve reference point strategies,and propose an elite archive strategy based on the R2 index.Thirteen benchmark test functions are used for verification,and the experiments show that it outperforms existing methods.The engineering application results show that the algorithm effectively reduces energy consumption in mine ventilation systems and improves fan shaft power and efficiency,proving its practicality.Third,an optimization method based on an improved pigeon swarm algorithm is proposed to further improve the intelligent control capabilities of complex dynamic systems.The method introduces Chebyshev filters to perform nonlinear changes on the compass operator of the pigeon swarm algorithm,introduces Levy flight to avoid local optima in the map search phase,introduces real-valued quantum coding methods to improve convergence direction,and improves pigeon elimination mechanisms.Applied to the mine intelligent air volume adjustment model,the results show that the proposed method has only two severe deviations in 150 air volume adjustments,with an accuracy rate of 98.6%,validating the method’s effectiveness for intelligent control of complex dynamic systems.Finally,to address the optimal control problem of complex dynamic systems,an optimal control method based on an improved equilibrium optimizer algorithm using adversarial learning machines is proposed.The algorithm introduces chaotic mapping to increase the diversity of initialization and introduces adversarial learning machines to accelerate convergence speed and avoid local optima.Results from 13 test functions show that the algorithm outperforms other methods.Applied to the non-stop switching system of the main ventilation fan in the mine,the results show that the algorithm effectively reduces the fluctuation of underground air volume and ensures the stable operation of the fan,verifying its practical application ability in optimal control of complex dynamic systems.