Echolocation Behavior Of Bats Research Articles

Solving the Cauchy problem for an elliptic equation using Bat Algorithm

This paper presents a method for solving a class of inverse problems for elliptic equations known as the data completion problem. The goal is to recover missing data on the inaccessible part of the boundary using measurements from the accessible part. The inherent difficulty of this problem arises from its ill-posed nature, as it is susceptible to variations in the input data. To address this challenge, the proposed approach integrates Tikhonov regularization to enhance the stability of the problem. To solve this problem, we use a metaheuristic approach, specifically, the Bat Algorithm (BA) inspired by the echolocation behavior of bats. The performed numerical results show that the Bat Algorithm yields stable, convergent, and accurate solutions.

Gravity data inversion applying a metaheuristic Bat algorithm for various ore and mineral models

Geophysical methods, especially the gravity method, are very helpful in ore and mineral explorations. Here, gravity modeling and interpretation for the subsurface geologic structures generally assumes either homogenous or spatially varying densities within target source rocks and surrounding structures. Therefore, the use of simple-geometric bodies helps in the validation of the subsurface ore and mineral targets. A Bat optimization algorithm is a recently developed metaheuristic algorithm that is used in various geophysical applications to explore and explain the parameters of buried ore and mineral targets. Using the Bat optimization algorithm, we were elucidating gravity anomaly profiles for ore and mineral cases. To perform global optimization, the Bat optimization algorithm is based on the echolocation behavior of bats. The global optimum solution in the Bat optimization algorithm reached the suggested minimum value of the objective function. The Bat optimization algorithm is applied to gravity data to estimate the target parameters (e.g., amplitude coefficient, depth, origin location, and geometric shape). The stability and efficiency of the introduced optimizing algorithm have been checked on two synthetic models represented in a spherical model and an infinitely horizontal cylinder model using two different kinds of noise. Furthermore, successful applications of the proposed algorithm for discovering the ore and minerals in Canada, Cuba, and India were presented. The results match well with the available geological and borehole information and other results from the published literature.

Power System Stabilizers Design Using BAT Algorithm

A new metaheuristic method, the BAT algorithm based on the echolocation behaviour of bats is proposed in this paper for optimal design of Power System Stabilizers (PSSs) in a multimachine environment. The PSSs parameter tuning problem is converted to an optimization problem which is solved by the BAT search Algorithm. An eigenvalues based objective function involving the damping factor, and the damping ratio of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed BAT based PSSs (BATPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under various operating conditions and disturbances. The results of the proposed BATPSS are demonstrated through time domain analysis, eigenvalues and performance indices. Moreover, the results are presented to demonstrate the robustness of the proposed algorithm over the GA and conventional one.

Analysis of echolocation behavior of bats in \u201cecho space\u201d using acoustic simulation

BackgroundEcholocating bats use echo information to perceive space, control their behavior, and adjust flight navigation strategies in various environments. However, the echolocation behavior of bats, including echo information, has not been thoroughly investigated as it is technically difficult to measure all the echoes that reach the bats during flight, even with the conventional telemetry microphones currently in use. Therefore, we attempted to reproduce the echoes received at the location of bats during flight by combining acoustic simulation and behavioral experiments with acoustic measurements. By using acoustic simulation, echoes can be reproduced as temporal waveforms (including diffracted waves and multiple reflections), and detailed echo analysis is possible even in complex obstacle environments.ResultsWe visualized the spatiotemporal changes in the echo incidence points detected by bats during flight, which enabled us to investigate the “echo space” revealed through echolocation for the first time. We then hypothesized that by observing the differences in the “echo space” before and after spatial learning, the bats’ attentional position would change. To test this hypothesis, we examined how the distribution of visualized echoes concentrated at the obstacle edges after the bats became more familiar with their environment. The echo incidence points appeared near the edge even when the pulse direction was not toward the edge. Furthermore, it was found that the echo direction correlated with the turn rate of the bat’s flight path, revealing for the first time the relationship between the echo direction and the bat’s flight path.ConclusionsWe were able to clarify for the first time how echoes space affects echolocation behavior in bats by combining acoustic simulations and behavioral experiments.

A discrete bat algorithm based on Lévy flights for Euclidean traveling salesman problem

Bat algorithm is a swarm-intelligence-based metaheuristic proposed in 2010. This algorithm was inspired by echolocation behavior of bats when searching their prey in nature. Since it first introduction, it continues to be used extensively until today, owing to its simplicity, easy handling and applicability to a wide range of problems. However, sometimes the major challenge faced by this technique is can be trapped in a local optimum when facing large complex problems. In this research work, a new discrete bat algorithm is proposed to solve the famous traveling salesman problem as NP-hard combinatorial optimization problem. To enhance the searching strategy and to avoid getting stuck in local minima, random walks based on Lévy's flights are combined with bat’s movement. In addition, to improve the diversity and convergence of the swarm, a neutral crossover operator is embedded to the proposed algorithm. To evaluate the performance of our proposal, two experiments are conducted on 38 benchmark datasets and the obtained results are compared with eight different approaches. Furthermore, the student’s t-test, the Friedman’s test and the post hoc Wilcoxon's test are performed to check whether there are significant differences between the proposed optimizer and the alternative techniques. The experimental results under comparative studies have shown that, in most cases, the proposed discrete bat algorithm yields significantly better results compared with its competitors.

Bat Algorithm Based on an Integration Strategy and Gaussian Distribution

The bat algorithm (BA) is a recent heuristic optimization algorithm based on the echolocation behavior of bats. However, the bat algorithm tends to fall into local optima and its optimization results are unstable because of its low global exploration ability. To solve these problems, a novel bat algorithm based on an integration strategy (IBA) is proposed in this paper. Through the integration strategy, an appropriate operator is adaptively selected to perform global search, so that the global search ability of the IBA is improved. Furthermore, the IBA disturbs the local optimum through a linear combination of Gaussian functions with different variances to avoid becoming trapped in local optima. The IBA also updates the velocity equation with an adaptive weight to further balance the exploration and exploitation. Moreover, the global convergence of the IBA is proved based on the convergence criterion of a stochastic algorithm. The performance of the IBA is evaluated on CEC2013 benchmark functions and compared with that of the standard BA as well as several of its variants. The results show that the IBA is superior to other algorithms.

Chaos-enhanced synchronized bat optimizer

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. Bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively.

Optimal design of damage tolerant composite using ply angle dispersion and enhanced bat algorithm

In this work, minimum weight optimization of laminated composite is performed using a newly developed enhanced bat algorithm (EBA). Bat algorithm (BA) is a recently developed swarm-based optimization technique which is inspired by the echolocation behavior of bats. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, the EBA is used to perform the design optimization of laminated composites. The laminate analysis based on classical laminate theory is utilized for the stress calculations. Tsai–Wu failure curve is considered as the constraint in this constrained optimization problem. Number of plies at each orientation angle are considered as the design variables. The design optimization has been carried out for both conventional and unconventional (dispersed plies) stacking sequences considering different loading configurations: uniaxial tension, biaxial tension with and without shear loadings. Ply angles dispersed in the range of $$5^{\circ }$$–$$85^{\circ }$$, $$25^{\circ }$$–$$65^{\circ }$$ and $$0^{\circ }$$–$$90^{\circ }$$ at intervals of $$5^{\circ }$$ are considered for the unconventional stacking sequence to increase damage tolerance. In addition, a new mathematical function is proposed to measure the dispersion of ply angles in the laminate called the dispersion function. Also, the performance of EBA is compared with standard BA in the optimum weight design of composite laminates.

Ultrasound navigation based on minimally designed vehicle inspired by the bio-sonar strategy of bats

ABSTRACTIn this study, we conducted vehicle experiments and a numerical simulation based on a simple algorithm inspired by the bio-sonar system of bats to investigate how the behavioral strategy employed by bats contributes to acoustic navigation for minimal-design sensing. In particular, a double-pulse scanning method inspired from the echolocation behavior of bats was proposed, in which (1) the direction of ultrasound emission by a vehicle equipped with 1 transmitter and 2 receivers was alternately shifted between the movement direction of the vehicle and the direction of the nearest obstacle, and (2) the movement direction of the vehicle was calculated for every double-pulse emission based on integrated information from all echoes detected. As a result of 100 repeated drives in a practical course, the success rate of an obstacle-avoidance drive improved from 13% with the conventional single-pulse scanning method to 73% with the proposed method. Furthermore, the numerical simulation demonstrated that the proposed method achieves robust path planning by suppressing the localization ambiguity due to the interference of multiple echoes. The practical experiments and numerical simulation suggest that bats employ a simple behavioral solution in the operation of acoustic sensing for various problems occurring in the real world.

An optimal dispatch model of power economic load system based on modified bat algorithm

The Economic load dispatch (ELD) problem considering valve-point effects, power balance constraint and the generating capacity constraint is a high-dimensional, discontinuous, non-differentiable, non-convex and nonlinear optimization problem, which cannot be solved by classical mathematics methods. In order to solve the problem, this paper presents a modified Bat Algorithm (MBA). Via simulating echolocation behavior of bats, the algorithm gets the optimal solution in the solution space. To enhance the overall performance and effectiveness of the proposed algorithm, the noninertia weight and hybrid mutation operation are integrated with the proposed method. To verify the effectiveness of the proposed method, the 3-unit test system, 13-unit test system and 40-unit test system were carried on the test. The simulation results obtained by the proposed algorithms are compared with the results using other recently developed methods which are available in the literature. From numerical results, it is found that the proposed MBA with noninertia weight and hybrid mutation operation approach is able to provide the better solution than other reported techniques in terms of fuel cost.

Modified bat algorithm based on covariance adaptive evolution for global optimization problems

Bat algorithm is a newly proposed swarm intelligence algorithm inspired by the echolocation behavior of bats, which has been successfully used in many optimization problems. However, due to its poor exploration ability, it still suffers from problems such as premature convergence and local optimum. In order to enhance the search ability of the algorithm, we propose an improved bat algorithm, which is based on the covariance adaptive evolution process. The information included in the covariance adaptive evolution diversifies the search directions and sampling distributions of the population, which is of great benefit to the search process. The proposed approaches have been tested on a set of benchmark functions. Experimental results indicate that the proposed algorithm obtains superior performance over the majority of the test problems.

Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions

This paper presents a maximum power point tracking (MPPT) method for photovoltaic system under partial shading conditions using bat algorithm (BA). The bat algorithm is a swarm intelligence based method which was inspired by the echolocation behaviour of bats. BA have a high accuracy in the global optimisation and it can provide good dynamic performance and very quick convergence rate by automatically switching between exploration and exploitation stages during the MPPT process. To verify the performance of the proposed method, several simulations have been carried out in Matlab/Simulink environment for various shading patterns. The simulations results highlight the accuracy of the proposed scheme for optimal management of the energy available at the output of the photovoltaic panels. In addition, the comparison with the P&O and the PSO methods shows that the proposed method outperforms them in term of global search ability and dynamic performance. To verify the practical implementation of the proposed method, a modular reconfigurable architecture is designed using very high speed integrated circuit hardware description language (VHDL) and implemented on Xilinx Virtex-5 (XC5VLX50-1FFG676) Field Programmable Gate Array (FPGA). The use of FPGA for designing the MPPT controller provides high performance, increases the robustness and makes the hardware implementation more flexible. The algorithm is tested in real time application on a buck-boost converter using a real photovoltaic panel. Experimental results confirm the efficiency of the proposed method in the global peak tracking and its high accuracy to handle the partial shading.

A Novel Bio Inspired Algorithm Based on Echolocation Mechanism of Bats for Seismic States Prediction

Bio-inspired algorithms are sort of implementation of natural solutions to solve hard problems – so called NP problems. A seismic hazard is the probability that an earthquake will occur in a given geographic area, within a given window of time, and with ground motion intensity exceeding a given threshold. Seismic hazards prediction is one of the fields where data mining plays an important role. This paper presents a new bio-inspired algorithm motivated by the echolocation behavior of bats for seismic hazard states prediction in coal mines based on previously recorded data. It is a distance calculation based approach, Results were very satisfactory in a manner that encourage us to continue working on this approach. The implementation of the algorithm touches three fields of studies, data discovery or so called data mining, bio inspired techniques, and seismic hazards predictions.

未知空間を飛行するコウモリの音響ナビゲーションアルゴリズムおよび自律走行車による工学的検証

未知空間を飛行するコウモリの音響ナビゲーションアルゴリズムおよび自律走行車による工学的検証

New directional bat algorithm for continuous optimization problems

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC’2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.

Improved bat algorithm for structural reliability assessment: application and challenges

Purpose– The first order reliability method requires optimization algorithms to find the minimum distance from the origin to the limit state surface in the normal space. The purpose of this paper is to develop an improved version of the new metaheuristic algorithm inspired from echolocation behaviour of bats, namely, the bat algorithm (BA) dedicated to perform structural reliability analysis.Design/methodology/approach– Modifications have been embedded to the standard BA to enhance its efficiency, robustness and reliability. In addition, a new adaptive penalty equation dedicated to solve the problem of the determination of the reliability index and a proposition on the limit state formulation are presented.Findings– The comparisons between the improved bat algorithm (iBA) presented in this paper and other standard algorithms on benchmark functions show that the iBA is highly efficient, and the application to structural reliability problems such as the reliability analysis of overhead crane girder proves that results obtained with iBA are highly reliable.Originality/value– A new iBA and an adaptive penalty equation for handling equality constraint are developed to determine the reliability index. In addition, the low computing time and the ease implementation of this method present great advantages from the engineering viewpoint.

An elitist non-dominated sorting bat algorithm NSBAT-II for multi-objective optimization of phthalic anhydride reactor

Nature inspired meta-heuristic algorithms are an integral part of modern optimization techniques. One such algorithm is bat algorithm which is inspired from echolocation behavior of bats and has been successfully applied to non-linear single-objective optimization problems. In this paper, a multi-objective extension of bat algorithm is proposed using the concepts of Pareto non-dominance and elitism. The novel algorithm is tested using thirty multi-objective test problems. The performance is measured using metrics namely, hyper-volume ratio, generational distance and spacing. The newly developed algorithm is then applied to a real-world multi-objective optimization problem of a phthalic anhydride reactor. It shows faster convergence for test problems as well as the industrial optimization problem than two popular nature inspired meta-heuristic algorithms, i.e. multi-objective non-dominated sorting particle swarm optimization and real-coded elitist non-dominated sorting genetic algorithm.

A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

ABSTRACTA new metaheuristic approach is presented to discover transition rules for a cellular automaton (CA) model using a novel bat movement algorithm (BA). CA is capable of simulating the evolution of complex geographical phenomena, and transition rules lie at the core of these models. An intelligence algorithm based on the echolocation behavior of bats is used to discover explicit transition rules for use in simulating urban expansion. CA transition rules are formed by links between attribute constraint items and classification items. The transition rules are derived using the BA to optimize the lower and upper threshold values for each attribute. The BA-CA model is then constructed for the simulation of urban expansion observed for Nanjing City, China. The total accuracy of newly formulated BA-CA model for this application is 86.9%, and the kappa coefficient is 0.736, which strongly suggest that the interactions of bats are effective in capturing the relationships between spatial variables and urban dynamics. It is further demonstrated that this bat-inspired BA-CA model performs better than the null model, the particle swarm optimization-based CA model (PSO-CA), and the ant colony optimization-based CA model (ACO-CA) using the same dataset. The model validation and comparison illustrate the novel capability of BA for discovering transition rules of CA during the simulation of urban expansion and potentially for other geographic phenomena.

Robust adaptive control of a bio-inspired robot manipulator using bat algorithm

This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction.

Optimal Synergetic Control based Bat Algorithm for DC-DC Boost Converter

In this paper, an optimal synergetic controller based on a bat algorithm for a DC-DC boost converter is presented. DC-DC boost converters are some of the most widely used power electronics devices for their high conversion efficiency and versatility. Bat optimisation based on the echolocation behaviour of bats, a bio-inspired algorithm, is successfully used for synergetic parameters optimization. The proposed control scheme is evaluated in a simulation and implementation study of a boost converter indicating satisfactory overall performance with stability insured through Lyapunov synthesis. Experimental results show that the proposed control provides good regulation.