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.
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.
Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature. Citation details: A. Chakri, X.-S. Yang, R. Khelif, M. Becouaret, Reliability based design optimization using the directional bat algorithm, Neural Computing and Applications, First published online, 2017. https://doi.org/10.1007/s00521-016-2797-3 3the time delay between the two ears, they can create a 3D mental image of their surrounding and determine if there is food or not. This behavior was the basic idea that has been used to develop the bat algorithm. Several studies showed that BA can solve optimization problems with higher efficiency compared to standard algorithms such as .Despite the fact that BA is a powerful optimization algorithm, it may suffer from the premature convergence that can occur under certain conditions, which is also true for all other algorithms such as PSO and GA. To overcome this problem, several techniques have been proposed to increase the exploitation and exploration capability of the algorithm. In [23], the authors proposed to use simulated annealing and Gaussian perturbation to speed up the convergence rate. In [24], the authors suggested to use chaotic maps to control the pulse rate and loudness. In [25], the authors recommended to use the Lévy flights and the differential operator to generate the bats ' movements and, in [26], the authors proposed to consider the bats' habitat selection and their self-adaptive compensation for the Doppler effect in the algorithm formulation. Other studies suggested hybridization between the standard BA and classical algorithm such as PSO [27], Artificial Bee Colony (ABC) [28], differential evolution [29,30] and Invasive Weed Optimization (IWO) [31].
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