Adaptive Pyramid Particle Swarm Algorithm with Competitive and Cooperative Strategies

Particle swarm optimization algorithms play an important role in various optimization problems by simulating the collaboration and information sharing among individuals in natural systems and using the local optimal solutions of individuals in the population to guide the global search. In this paper, based on the particle swarm algorithm with pyramid topology, the adaptive pyramid particle swarm algorithm (APPSO) is proposed by innovating the update of particles through the use of parameter adaptive competition and cooperation strate-gies. The algorithm adds adaptive strategies to improve the accuracy performance of the algorithm and introduces the concept of iterative stagnation to maintain the diversity of the population. In this paper, the proposed algorithm (APPSO) is compared with six algorithms on the CEC2022 test set, and extensive experimental results show that APPSO has the ability to accurately converge to the global optimum.