Abstract:
Optimisation remains inevitable in any organisation as the need to maximise the
limited resources persists. It justifies the seemingly endless research in this area. This study
explores the effectiveness of chaos to mitigate false or premature convergence problem in
African buffalo optimisation (ABO) algorithm. Chaos employs the ergodic and stochastic
properties to handle this limitation. Three resourceful chaotic functions in the literature are
evaluated to find the best strategy for ABO improvement. The same strategy is applied across the
algorithms under study to provide an unbiased judgment. The study validates the proposed
system’s performance with a range of nonlinear test functions. The proposed system’s result is
compared with standard ABO, Particle swarm optimisation (PSO), and chaotic particle swarm
optimisation (CPSO) algorithms. Although chaotic ABO (CABO) gave 92% performance in
comparison with standard ABO, chaotic PSO, and standard PSO; it requires further investigation.
To be more explicit, the reason for no significant difference between chaotic-ABO and standard
ABO in some functions calls for further research attention. The present study also highlights the
research future scope. In all, the study gives insight to researchers on the appropriate algorithm
for a real-world problem.
