An Echo-Aided Bat Algorithm to Support Measurable Movement for Optimization Efficiency

An Echo-Aided Bat Algorithm (EABA) based on measurable movement is proposed to improve optimization efficiency in this study. The conception is to employ the echo time to measure the distance from bats and objective. The bats emit an ultrasound to objective to measure the time of a round trip between their position and objective position. The echo time can guide the bats to correct velocity, direction and movement step. And the bats can more accurately measure the position of objective to adjust its step to find the better solution. There are many scenarios with different population sizes and objective functions to verify the performance of the proposed EABA. The experimental numeric result shows that EABA has better ability of search to improve the quality of the best solution than BA. The solution performance is improved by 45% and 30% for the functions of low complexity and high complexity in comparison with the original bat algorithm, respectively.