bat algorithm (BA) is a new stochastic optimization technique for global optimization. In the paper, we introduce both simulated annealing and Gaussian perturbations into the standard bat algorithm so as to enhance it...
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bat algorithm (BA) is a new stochastic optimization technique for global optimization. In the paper, we introduce both simulated annealing and Gaussian perturbations into the standard bat algorithm so as to enhance its search performance. As a result, we propose a simulated annealing Gaussian bat algorithm (SAGBA) for global optimization. Our proposed algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also speeds up the global convergence rate. We have used BA, simulated annealing particle swarm optimization and SAGBA to carry out numerical experiments for 20 test benchmarks. Our simulation results show that the proposed SAGBA can indeed improve the global convergence. In addition, SAGBA is superior to the other two algorithms in terms of convergence and accuracy.
bat algorithm is a novel branch of evolutionary computation. Although there are several research papers that focus on this new algorithm, however, few of them concerns the high-dimensional numerical problems. In this ...
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bat algorithm is a novel branch of evolutionary computation. Although there are several research papers that focus on this new algorithm, however, few of them concerns the high-dimensional numerical problems. In this paper, a new variant called bat algorithm with Gaussian walk (BAGW) is proposed aiming to solve this problem. In this variant, a Gaussian walk is employed in the local turbulence instead of the original uniform walk to improve the local search capability. Furthermore, to keep the high exploitation pressure, the velocity update equation is also changed. Finally, to increase the population diversity, the frequency is dominated by each dimension in our modification, as well as it is depended on the different bat in the standard version. To test the performance of our variant, four famous un-constraint numerical benchmarks are employed, and test on different dimensional cases, simulation results show our modification is effective.
The bat algorithm (BA) is a novel evolutionary optimization algorithm, most studies of which have been performed with low-dimensional problems. To test and improve the global search ability of BA with large-scale prob...
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The bat algorithm (BA) is a novel evolutionary optimization algorithm, most studies of which have been performed with low-dimensional problems. To test and improve the global search ability of BA with large-scale problems, two new variants using principal component analysis (PCA_BA and PCA_LBA) are designed in this paper. A correlation threshold and generation threshold are determined using the golden section method to enhance the effectiveness of this new strategy. To test performance, CEC'2008 large-scale benchmark functions are utilized and compared with other algorithms;simulation results indicate the validity of this modification.
bat algorithm with Gaussian walk (BAGW) is a new variant of bat algorithm aiming to improve the computational efficiency. In BAGW, a Gaussian walk is employed in the local turbulence instead of the original uniform wa...
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bat algorithm with Gaussian walk (BAGW) is a new variant of bat algorithm aiming to improve the computational efficiency. In BAGW, a Gaussian walk is employed in the local turbulence instead of the original uniform walk, so that the search efficiency of exploitation capability is controllable. Furthermore, to keep the high exploitation pressure, the velocity update equation is also changed. In this paper, BAGW is applied to solve the directing orbits of chaotic systems. To test its performance, the standard version bat algorithm and bat algorithm with Levy flight are employed to compare, simulation results show BAGW is superior to other two algorithms.
Toy model is a conceptually simple model for protein-folding phenomena in which there are only two different "amino acids" are considered. Due to the amount of local optima, the precise structure of toy mode...
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Toy model is a conceptually simple model for protein-folding phenomena in which there are only two different "amino acids" are considered. Due to the amount of local optima, the precise structure of toy model is hardly to be estimated, therefore, many bio-inspired algorithms are applied to it. In this paper, a novel bio-inspired algorithm, bat algorithm which is inspired by the echolocation behaviour, is applied to solve this problem. To illustrate the efficiency, short sequences, Fibonacci sequences and real protein sequences are used to compare, simulation results show it is effective.
One of the major objectives of image analysis is to identify an object in the image or identify different objects or regions separately. The role of segmentation is very important in this identification process. The p...
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One of the major objectives of image analysis is to identify an object in the image or identify different objects or regions separately. The role of segmentation is very important in this identification process. The process of segmentation becomes more complex in case of colour images. Different techniques are used to find the optimal threshold values for segmenting an image. In this paper, optimal threshold values have been calculated using bat algorithm and maximizing different objective function values based on Kapur's entropy, Tsallis entropy, Otsu's method, Shannon entropy, Renyi entropy. A comparative analysis of RMSE, PSNR, CPU time, Jaccard similarity coefficient, accuracy obtained for different objective functions are prepared using Lena image at different threshold levels. The experimental results and accuracy obtained prove the effectiveness of the proposed method.
The problem of system identification concerns with the design of adaptive infinite impulse response (IIR) system by determining the optimal system parameters of the unknown system on the minimization of error fitness ...
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The problem of system identification concerns with the design of adaptive infinite impulse response (IIR) system by determining the optimal system parameters of the unknown system on the minimization of error fitness function. The conventional system identification techniques have stability issues and problem of degradation in performance when modeled using a reduced-order system. Hence, a meta-heuristic optimization method is applied to overcome such drawbacks. In this paper, a new meta-heuristic optimization algorithm, called bat algorithm (BA), is utilized for the design of an adaptive IIR system in order to approximate the unknown system. bat algorithm is inspired from the echolocation behavior of bats combining the advantages of existing optimization techniques. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. The proposed BA method for system identification is free from the problems encountered in conventional techniques. To valuate the performance of the proposed method, mean square error, mean square deviation and computation time are measured. Simulations have been carried out considering four bench-marked IIR systems using the same-order and reduced-order systems. The results of the proposed BA method have been compared to that of the well known optimization methods such as genetic algorithm, particle swarm optimization and cat swarm optimization. The simulation results confirm that the proposed system identification method outperforms the existing system identification methods.
In this paper, a multiobjective hybrid bat algorithm is proposed to solve the combined economic/emission dispatch problem with power flow constraints. In the proposed algorithm, an elitist nondominated sorting method ...
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In this paper, a multiobjective hybrid bat algorithm is proposed to solve the combined economic/emission dispatch problem with power flow constraints. In the proposed algorithm, an elitist nondominated sorting method and a modified crowding-distance sorting method are introduced to acquire an evenly distributed Pareto Optimal Front. A modified comprehensive learning strategy is used to enhance the learning ability of population. Through this way, each individual can learn not only from all individual best solutions but also from the global best solutions (nondominated solutions). A random black hole model is introduced to ensure that each dimension in current solution can be updated individually with a predefined probability. This is not only meaningful in enhancing the global search ability and accelerating convergence speed, but particularly key to deal with high dimensional systems, especially large-scale power systems. In addition, chaotic map is integrated to increase the diversity of population and avoid premature convergence. Finally, numerical examples on the IEEE 30-bus, 118-bus and 300-bus systems, are provided to demonstrate the superiority of the proposed algorithm.
The power output curves of solar photovoltaic (PV) system have multiple peaks under partially shaded condition. As the same as traditional MPPT (Maximum Power Point Tracking) search methods, bat algorithm often makes ...
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The power output curves of solar photovoltaic (PV) system have multiple peaks under partially shaded condition. As the same as traditional MPPT (Maximum Power Point Tracking) search methods, bat algorithm often makes optimized results fall into local extremum. So an improved bat algorithm is proposed. Chaos search strategy is introduced in initial arrangement to improve the uniformity and ergodicity of population. Adapting weight is introduced to balance the global searching ability and the local searching ability. Dynamic contraction regain decreases the search range more effectively. Compared with the original algorithm, the rapidity and accuracy of algorithm have been improved. The simulation shows that improved bat algorithm can find the globally optimal point fast, with high precision, under the partially shaded condition. (C) 2017 Elsevier B.V. All rights reserved.
This paper proposes a bat algorithm （BA） based Control Parameterization and Time Discretization （BA-CPTD） method to acquire time optimal control law for formation reconfiguration of multi-robots system. In this me...
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This paper proposes a bat algorithm （BA） based Control Parameterization and Time Discretization （BA-CPTD） method to acquire time optimal control law for formation reconfiguration of multi-robots system. In this method, the problem of seeking for time optimal control law is converted into a parameter optimization problem by control parameterization and time discretization, so that the control law can be derived with BA. The actual state of a multi-robots system is then introduced as feedback information to eliminate formation error. This method can cope with the situations where the accurate mathematical model of a system is unavailable or the disturbance from the environment exists. Field experiments have verified the effectiveness of the proposed method and shown that formation converges faster than some existing methods. Further experiment results illustrate that the time optimal control law is able to provide smooth control input for robots to follow, so that the desired formation can be attained rapidly with minor formation error. The formation error will finally be eliminated by using actual state as feedback.
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