Cascade PID controller optimization using bison algorithm

Meta-heuristic algorithms are reliable tools for modern optimization. Yet their amount is so immense that it is hard to pick just one to solve a specific problem. Therefore many researchers hold on known, approved algorithms. But is it always beneficial? In this paper, we use the meta-heuristics for the design of cascade PID controllers and compare the performance of the newly developed Bison Algorithm with well-known algorithms like the Differential Evolution, the Genetics Algorithm, the Particle Swarm Optimization, and the Cuckoo Search. Also, in the proposed approach, the controller parameters were encoded to increase the chance of reducing the controller structure, and thus facilitate the automatic selection of its configuration. The simulations were performed for three different control problems and checked whether the use of cascade structures could bring significant benefits in comparison to the use of classic PID controllers. © 2020, Springer Nature Switzerland AG

Introducing the run support strategy for the bison algorithm

Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to ensure, that the newly discovered solutions can affect the overall optimization process. In this paper, we propose a new Run Support Strategy for the Bison Algorithm, that should enhance the utilization of newly discovered solutions, and should be suitable for both continuous and discrete optimization. © Springer Nature Switzerland AG 2020