Introducing the Run Support Strategy for the Bison Algorithm

Abstract

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.

How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?

“…To analyze the impact of a higher number of objective function evaluations, we compared the optimization performance of selected algorithms solving two benchmark sets of problems IEEE CEC 2015 and 2017. The examined swarm algorithms were: the Particle Swarm Optimization [58], the Cuckoo Search [59], the Bat Algorithm [60], the Firefly Algorithm [61], and the Bison Algorithm [62]. Further we analysed the impact of FEs budget on the winners of the CEC benchmark competitions: the L-SHADE with Eigenvector Crossover and Successful Parent-Selecting Framework [63], and the Effective Butterfly Optimizer with Covariance Matrix [64].…”

Cascade PID Controller Optimization Using Bison Algorithm

“…The Bison Algorithm is a recent swarm algorithm inspired by the behavior of bison herds [24]. When bison are in danger, they form a circle with the strongest on the outline, trying to protect the weak ones inside.…”

“…However, the increase in complexity of the solved problems causes new population algorithms to find their place (see, e.g., [14,18]). New algorithms are using a variety of different mechanisms to overcome the optimization problems [19], such as population division into subgroups with different behaviour [20], dynamic adaptation of parameters [21,22], population restart [23], or boosting the exploration of the feasible solution area [24].…”

“…This approach frees the user from the need to design the exact controller structure. To cope with that, a Bison Algorithm (BA [24]) is used to optimize the controller parameters. The idea of this paper is also to check whether a new algorithm, such as BA, allows one to get better results compared to classic population-based algorithms.…”