To overcome the disadvantages of traditional genetic algorithms, which easily fall to local optima, this paper proposes a hybrid genetic algorithm based on information entropy and game theory. First, a calculation of the species diversity of the initial population is conducted according to the information entropy by combining parallel genetic algorithms, including using the standard genetic algorithm (SGA), partial genetic algorithm (PGA) and syncretic hybrid genetic algorithm based on both SGA and PGA for evolutionary operations. Furthermore, with parallel nodes, complete-information game operations are implemented to achieve an optimum for the entire population based on the values of both the information entropy and the fitness of each subgroup population. Additionally, the Rosenbrock, Rastrigin and Schaffer functions are introduced to analyse the performance of different algorithms. The results show that compared with traditional genetic algorithms, the proposed algorithm performs better, with higher optimization ability, solution accuracy, and stability and a superior convergence rate.

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

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Partial transmit sequence (PTS) is an effective method to reduce the peak‐to‐average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) system, but it is not suitable for the filter bank multi‐carrier with offset quadrature amplitude modulation (FBMC/OQAM) system because of the overlapping structure of FBMC/OQAM signals. And PTS needs the traversal method to search for the optimal solution, which will increase the complexity of the system. To overcome this shortcoming, a PTS based on discrete particle swarm optimization with threshold (PTS‐DPSO‐TH) is proposed and applied to FBMC/OQAM system in this paper. PTS‐DPSO‐TH uses discrete particle swarm optimization (DPSO) to search for the optimal solution, effectively reducing the excessively high PAPR of the FBMC/OQAM system and avoiding the increase of system complexity. Secondly, the threshold is introduced to decrease the number of iterations and further reduce the system's complexity on the premise of guaranteeing the performance of PAPR reduction. Furthermore, through simulation, the effects of multiple parameters on PAPR reduction performance of PTS‐DPSO‐TH are compared and analysed.