Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm and Its Application

Aiming at the problems of uneven population initialization distribution, easy trapping in local optima, unbalanced exploration and exploitation capabilities, insufficient optimization accuracy and convergence speed of the original Greater Cane Rat Algorithm (GCRA), this paper proposes a Chaos-Integrated Difference-Enhanced Greater Cane Rat Algorithm (CEGCRA). Firstly, the algorithm adopts the piecewise chaotic map to generate the initial population, which effectively improves the uniformity and diversity of the population and reduces the risk of premature convergence. Secondly, an accumulated difference foraging strategy is designed to integrate the position and fitness difference information between individuals and the optimal individual, dynamically adjust the search direction and step size, and realize the adaptive balance between global exploration and local exploitation capabilities. Finally, the dynamic switching mechanism between the exploration and exploitation stages of the algorithm is improved, and the boundary constraint handling strategy is optimized to further enhance the algorithm stability. To verify the performance of the CEGCRA, comparative experiments were carried out on the CEC2014 and CEC2020 benchmark test suites. The results show that compared with the original GCRA, the optimal fitness value of the CEGCRA is reduced by an average of 35.3%, the standard deviation is reduced by an average of 22.7%, and the convergence speed is increased by an average of 28.9%. In two typical engineering constrained optimization problems, namely, welded beam design and cantilever beam design, the cost of the welded beam solved by the CEGCRA is 12.5% lower than that of the original GCRA and 8.7% lower than that of the PSO algorithm; the weight of the cantilever beam is 0.012% lower than that of the original GCRA and 0.008% lower than that of the GA, with a constraint satisfaction rate of 100%. The experimental results fully prove that the CEGCRA is superior to the original GCRA and seven comparison algorithms such as PSO, DE and SSA in terms of optimization accuracy, convergence speed, robustness and constraint handling ability and can effectively solve complex engineering optimization problems with high dimensionality, nonlinearity and multiple constraints.