Swarm Intelligence Algorithms

52 Bat Algorithm

This gives f (x

1

1

) = 5.1882, which is better than x

best

. So we update it as the

new best solution

x

best

= [1.8060, 2.0485, 2.8545]. (4.20)

Similarly, we do this for the rest of the other four solutions in the popula-

tion. Once the first iteration is done, we vary r and A again, and then carry

out another loop over the whole population.

The bat algorithm can be efficient. For the objective function, the optimal

solution is x

∗

= [1, 1, 1]. Using the above codes, we can typically get f

best

=

4.9 × 10

−16

after 500 iterations.

4.6 Conclusion

The bat algorithm is a simple, flexible and yet efficient algorithm for solv-

ing optimization problems. In