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Bat Algorithm
Published in A Vasuki, Nature-Inspired Optimization Algorithms, 2020
The bat algorithm (BA) is a metaheuristic algorithm based on the echolocation behavior of bats. In developing the algorithm the following simplifying assumptions are made: Bats fly randomly with a fixed velocity and take up different positions. Their pulse emissions have varying frequencies, wavelengths, and loudness which they use to search for prey. The rate of pulse emissions and the frequency are adjusted based on the distance of the bat to the prey. Bats use echolocation to detect as well as differentiate between prey (food) and other objects, and they are able to sense distance. The loudness of the ultrasonic pulses emitted by bats varies from a minimum value to a maximum value.
Artificial Bee Colony, Firefly Swarm Optimization, and Bat Algorithms
Published in Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 2018
The bat algorithm has a diverse area of application such as continuous and combinatorial optimization, image processing, clustering, engineering optimization, etc. The unique feature of BA is that it is stimulated by the echolocation behaviour of bats, and the performance of BA is comparatively better than other competitive algorithms considered (Yang & He, 2013a). It has fewer control parameters and is easy to implement. Some of the recent developments on BA were discussed in previous sections. In (Mirjalili et al., 2014), BA is applied on various benchmark problems with diverse characteristics such as uni-model and multi-model. Mirjalili et al. (2014) solved optical buffer design problems from optical engineering. Photonic crystal (PC) is very popular due to its extensive variety of applications because the photonic crystal waveguide (PCW) has applications in time-domain signal processing, non-linear optics, and all-optical buffers. The new variant of BA was successfully used for PCW design with better efficiency. The BA also has been applied to combinatorial optimization and scheduling problems, parameter estimation, classifications, data mining, clustering, and image processing, and in fuzzy systems to design various complex machinery.
Dialectics of Nature: Inspiration for Computing
Published in Nazmul Siddique, Hojjat Adeli, Nature-Inspired Computing, 2017
The bat algorithm (BatA) is based on the echolocation behavior of bats. The capability of micro-bats is fascinating as these micro-bats use a type of sonar, called, echolocation, to detect prey, avoid obstacles, and locate their roosting crevices in the dark. These bats emit a very loud sound pulse and listen for the echo that bounces back from the surrounding objects. Their pulses vary in properties and can be correlated with their hunting strategies, depending on the species. By comparing the outgoing pulse with the returning echoes, the brain and auditory system can produce detailed images of the bat's surroundings. This allows bats to detect, localize, and even classify their prey in complete darkness. If the echolocation characteristics of micro-bats are idealized in some way, various bat-inspired search algorithms can be developed. Yang (2010a, 2011) simulated echolocation behavior of bats and its associated parameters in a numerical optimization algorithm. The interesting simplification is that no ray tracing is used in estimating the time delay and three-dimensional topography.
Water distribution in community irrigation using a multi-agent system
Published in Journal of the Royal Society of New Zealand, 2023
Kitti Chiewchan, Patricia Anthony, Birendra KC, Sandhya Samarasinghe
To address water scarcity, a stochastic multi-objective nonlinear programming model was developed to balance the conflicting objectives of maximising both net economic benefit (NEB) and irrigation water use efficiency (IWUE)(Yan et al. 2021). A case study was conducted in north-west China to demonstrate the applicability of this model and the results showed that the model can generate solutions that save irrigation water while ensuring NEB. Water resources scarcity was also studied by Zarei et al. (2019), who implemented an evolutionary hybrid algorithm (that integrates the bat algorithm and particle swarm optimisation (PSO)) together with game theory for the Shahid Dam Reservoir in Iran. The bat algorithm is a heuristic optimisation algorithm based on echolocation behaviour of bats developed in 2010 (Yang 2010). PSO is a population-based stochastic approach for solving continuous and discrete optimisation problems (D. Wang et al. 2018b). The hybrid algorithm was used to calculate the monthly volume of needed water. Then, the proportional method in game theory was used to allocate the released water from the reservoir to each downstream need. This method was found to be superior to the bat algorithm and PSO.
Review on automatic generation control strategies for stabilising the frequency deviations in multi-area power system
Published in International Journal of Ambient Energy, 2022
K. Peddakapu, M. R. Mohamed, P. Srinivasarao, A. S. Veerendra, D. J. K. Kishore, P. K. Leung
Bat algorithm was proposed by Xin-She Yang in 2010 and it is a metaheuristic algorithm for finding global difficulties. It was motivated by the echolocation behaviour of micro-bats with changing pulse rates of emission and loudness. The bat algorithm was penetrated in the LFC system for optimising the fuzzy controller and determine the power quality problems in a multi-area system (Khooban and Niknam 2015). Likewise, Model predictive controllers were suggested to damped out the unnecessary oscillations in two area AGC systems using an inspired bat algorithm (Elsisi et al. 2016). Thus, the bat algorithm was presented an excellent dynamic performance in respect of settling time, rise time, and peak overshoot at an extensive range of system parameter uncertainties. Nevertheless, the bat algorithm has slow convergence as well as does not have proper mathematical analysis to connect the parameters with required convergence rates and difficult to find the best values as per applications.
JAYA Optimized Generation Control Strategy for Interconnected Diverse Source Power System with Varying Participation
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
Nidhi Gupta, Narendra Kumar, B. Chittibabu
Like Particle Swarm Optimization (PSO), BAT algorithm is a meta-heuristic optimization algorithm which was first considered by YANG (Gandomi and Yang 2014). PSO and BAT algorithms consider the position and velocity as dual control parameters. Unlike PSO, BAT algorithm considers three more parameters, namely loudness, pulse rate, and frequency. This algorithm is based on the simulated natural behavior of bats while searching for food. In this algorithm, echolocation works as a sonar which is applied to differentiate between food and prey. In the search of food, they emit a loud and short pulse of sound. When sound produced by bat strike the food, the echo reverts back to them. This whole process completed in a fraction of seconds and they get an idea of the distance from the food in the dark also. They adjust their loudness magnitude with respect to the distance and food. They produce a loud sound when they are away from the food and produces base sound when becomes closer to the food. This concept is used in our model in finding the optimized values of parameters. The process of updating position, velocity, and frequency of bat during optimization is according to the following Equations 2–4, which are given as: