In order to solve the problem of slow convergence speed and low convergence precision inherent in the clone selection algorithm

a bilevel coevolutionary clonal selection algorithm is proposed. The algorithm used different evolutionary schemes for each level of evolution to search for optimization. Through information sharing

co-evolution between levels was realized. Moreover

an evolutionary model of intra-level competition and inter-level cooperation was formed. By constructing a hybrid co-evolution mechanism of multi-evolutionary strategies

it could realize the complementary advantages and information increment of different evolutionary strategies in the optimization process. Thus

the purpose of effectively balancing the global exploration and local exploitation of the algorithm was achieved. Simultaneously

the premature convergence problem of the algorithm could also be better avoided. The feasibility and effectiveness of the proposed algorithm were verified by 10 benchmarks. Experimental results show that the proposed algorithm had obvious advantages such as stronger global search ability

better stability

faster convergence speed

higher convergence accuracy

and so on. Furthermore

these advantages became more prominent with the increase of testing dimensions. Lorenz chaotic system was taken as an example to test the algorithm in estimating the parameters. Simulation results confirmed that the proposed algorithm can be used for high-precision estimation of system parameters

and it is an effective parameter estimation method for chaotic systems.