concept

bat algorithm

Also known as: BA, BAT

Facts (8)

Sources

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 5 facts

measurementThe BAT Algorithm reduces the peak-to-low energy demand difference to 2.44 MW in the residential sector and 1.64 MW in the IT sector.

referenceIn the Bat Algorithm, frequency (fi) represents the rate of change in solutions, velocity (vi) correlates with the speed of convergence in the search space, loudness (Ai) adjusts the exploration and exploitation balance, and pulse emission rate (ri) determines the rate of local search around the current best solution.

claimThe Bat Algorithm (BA) simulates the echolocation behavior of bats, where bats emit sound waves and listen to echoes to navigate and locate prey, to search for optimal solutions in a problem space.

procedureThe Bat Algorithm for demand-side management proceeds in four steps: (1) Initialization: Generate the initial population of bats, each with a random position (Xi) and velocity (vi) within defined constraints. (2) Fitness Evaluation: Calculate the fitness of each bat, defined as the peak demand (Dpeak) for its corresponding demand-side management schedule. (3) Loop Until Convergence: Update bats' positions and velocities based on frequency, velocity, and position update rules; perform local search if a random number is greater than the pulse emission rate (ri); update loudness (Ai) and pulse emission rate (ri) as iterations proceed; accept new solutions if they have better fitness and a random number is less than the loudness. (4) Loop until a predetermined condition is met with minimum peak demand (Dpeak).

procedureThe Bat Algorithm for Demand Side Management follows this procedure: 1: Initialize the bat population Xi and velocities vi for i = 1 to N. 2: Define Pulse frequency fi at xi. 3: Initialize pulse rates ri and the loudness Ai. 4: While (t < Itermax) or (convergence criteria is not met) do. 5: for each bat i = 1 to N do. 6: Generate new solutions by adjusting frequency, and updating velocities and positions. 7: if (rand > ri) then. 8: Select a solution among the best solutions. 9: Generate a local solution around the selected best solution. 10: end if. 11: Generate a new solution by flying randomly. 12: if (rand < Ai && f(Xi) < f(X*)) then. 13: Accept the new solution. 14: Increase ri and reduce Ai. 15: end if. 16: Rank the bats and find the current best X*. 17: end for. 18: t = t + 1. 19: end while.

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 3 facts

claimIn the context of energy management optimization, BAT stands for Bat algorithm.

referenceYang X-S and He X (2013) published a literature review and analysis of applications for the Bat algorithm in the International Journal of Bio-Inspired Computation.

claimJavaid et al. developed the bat-crow search algorithm (BCSA) by combining the meta-heuristic bat algorithm (BA) and the crow search algorithm (CSA) for Home Energy Management Systems (HEMS) using critical peak pricing (CPP).