Improved Phasmatodea Population Evolution Algorithm based on Dung Beetle Optimizer

To address the issues of low convergence accuracy, vulnerability to local optima, and prolonged execution time associated with traditional Phasmatodea population evolution algorithms, we propose a revised approach known as the Phasmatodea Population Evolution Algorithm based on the Dung Beetle Optimizer (DPPE). Initially, we eliminate the competitive mechanism and replace the acceptance criteria for new solutions with a greedy rule following mixed disturbances, which significantly reduces the computational time of the algorithm. Next, we employ the PWLCM chaotic mapping method for population initialization, enhancing the quality of the initial solutions. Additionally, we integrate the Dung Beetle Optimizer with a crossover strategy to boost the convergence accuracy of the algorithm. Lastly, we introduce the Gaussian-Cauchy mixed mutation operator to mitigate the likelihood of encountering local optima. To evaluate the efficacy of the DPPE algorithm, we benchmark it against other optimization algorithms using 11 recognized test functions. The results indicate that the DPPE algorithm outperforms its competitors. Furthermore, to underscore the potential practical applications of the DPPE algorithm, we successfully implement it in engineering design problems, where experimental findings demonstrate its capability to effectively address real-world challenges.