Aiming at solving a drawback of the second-order beetle antenna search
(SOBAS), a variant of the beetle antenna search (BAS), that it is difficult
to find the global optimal solution and the low convergence accuracy when
applied to the multimodal optimization functions with high dimension or
large variable region, a chaotic-based second-order BAS algorithm (CSOBAS)
is proposed by introducing chaos theory into the SOBAS. The algorithm mainly
has three innovations: 1) chaos initialization: choosing the one with the
smallest fitness function value from twenty beetles with different positions
for iterative search; 2) using chaotic map to tune the randomization
parameter in the detection rule; 3) imposing a chaotic perturbation on the
current beetle to hope to help the search to jump out the local optimal
solution. Eight different chaotic maps are used to demonstrate their impact
on the simulation results. With six typical multimodal functions,
performance comparisons between the CSOBAS and the SOBAS are conducted,
validating the effectiveness of the CSOBAS and its superiority compared to
the SOBAS. What?s more, the CSOBAS with an appropriate chaotic map can
achieve a very good convergence quality compared to other swarm intelligence
optimization algorithms while maintaining an individual.