Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

Abstract

The bat algorithm (BA) is a new bionic intelligent optimization algorithm to simulate the foraging behavior and the echolocation principle of the bats. The parameter initialization of the discussed binary bat algorithm (BBA) has important influence on the convergence speed, convergence precision, and good global searching ability of the BBA. The convergence speed and algorithm searching precision are determined by the pulse of loudness and pulse rate. The simulation experiments are carried out by using the six typical test functions to discuss this influence. The simulation results show that the convergence speed of the BBA is relatively sensitive to the setting of the algorithm parameters. The convergence precision reduces when increasing the rate of bat transmitted pulse alone and the convergence speed increases the launch loudness alone. The proper combination of BBA parameters (the rate of bat transmitted pulse and the launch loudness) can flexibly improve the algorithm's convergence velocity and improve the accuracy of the searched solutions.

A Framework for Profit Maximization in a Grid-Connected Microgrid With Hybrid Resources Using a Novel Rule Base-BAT Algorithm

“…To adjust the bat position, the authors in [36] advised a position updating way that depends on a sigmoid transfer function. This scheme has a demerit as the bats positions will remain unchanged when their velocity values continues to increase [37]. The authors analyze the effect of loudness and pulse rate values on the convergence speed and accuracy of the BAT algorithm in [37].…”

“…This scheme has a demerit as the bats positions will remain unchanged when their velocity values continues to increase [37]. The authors analyze the effect of loudness and pulse rate values on the convergence speed and accuracy of the BAT algorithm in [37]. It is found that the convergence speed and precision of BAT technique improve by increasing the pulse rate and decreasing loudness of bats.…”

“…r b,it+1 = α r r b,it (36) w b,it+1 = α w w b,it (37) where, α a , α r and α w are loudness weight factor, pulse rate weight factor, and velocity damping factor, respectively. Here, a new update equation of the bat velocity weight factor w b,it is introduced.…”

“…If the continuity condition is satisfied, modify the bat position and best individual solution as in (33)- (34). Also, update the loudness level, pulse rate, and velocity weight factor as in (35)- (37). Otherwise, move to step 9.…”

Particle Swarm Optimization With Probability Sequence for Global Optimization

“…To deal with these complex optimization problems, sophisticated and intelligent algorithms are required. The ultimate goal of the optimization is to reach the best possible solution [3].…”