This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm...
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This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA-LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA-LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA-LSS can be promise alternative for solving large-scale 0-1 knapsack problems.
The most effective parameter on the value of mining projects is metal price volatility. Therefore, knowing the metal price volatility can help the managers and shareholders of the mining projects to make the right dec...
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The most effective parameter on the value of mining projects is metal price volatility. Therefore, knowing the metal price volatility can help the managers and shareholders of the mining projects to make the right decisions for extending or restricting the mining activities. Nowadays, classical estimation methods cannot correctly estimate the metal prices volatility due to their frequent variations in the past years. For solving this problem, it is necessary to use the artificial algorithms that have a good ability to predict the volatility of the various phenomena. In this paper, the bat algorithm was used to predict the copper price volatility. Accordingly, Brownian motion with mean reversion (BMMR) was chosen as the most suitable time series function with the root mean square error (RMSE) of 0.449. Then, the estimation parameters of the equation were modified using bat algorithm. Finally, it is concluded that the determined equation with 0.132 of RMSE can predict the copper price better than the classic estimation methods.
This paper proposes an adaptive null-steering beamformer based on bat algorithm (BA) for Uniform Linear Array (ULA) antennas to suppress the interference. The beamformer is targeted at steering nulls of ULA pattern in...
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This paper proposes an adaptive null-steering beamformer based on bat algorithm (BA) for Uniform Linear Array (ULA) antennas to suppress the interference. The beamformer is targeted at steering nulls of ULA pattern in the directions of the interferences. The amplitude-only nulling method has been utilized for adjusting excitation weight of each array element. In order to validate the proposal, several scenarios of ULA array pattern imposed with the prescribed nulls have been investigated and compared with those of accelerated particle optimization (APSO) and genetic algorithm (GA). The proposed beamformer has shown the ability to suppress sidelobes and to place precisely single, multiple, and broad nulls at arbitrary interference directions. Furthermore, the beamformer is much faster and more effective than APSO and GA-based ones.
Femtocell networks can enhance indoor coverage and increase system capacity, but in the spectrum-shared Orthogonal Frequency Division Multiple Access (OFDMA) femtocell networks, peer-to-peer interference between femto...
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ISBN:
(纸本)9781538674130
Femtocell networks can enhance indoor coverage and increase system capacity, but in the spectrum-shared Orthogonal Frequency Division Multiple Access (OFDMA) femtocell networks, peer-to-peer interference between femtocells and Cross-layer interference between femtocells and Macrocells severely limits system performance. For this kind of interference, a bat algorithm based on decreasing inertia weight is proposed. Taking the maximization of system throughput as the optimization goal, under the premise of satisfying the quality of service (Qos) of heterogeneous network users, the channel resource allocation of spectrum resources is performed using the improved bat algorithm, and the performance of the algorithm is compared with the genetic algorithm. Simulation results show that the number of subchannels affects the network system capacity. Experiments show that in a heterogeneous network, the bat algorithm is used to allocate subchannels to improve the system capacity.
Modern communication networks offer advance and diverse applications, which require huge usage of network resources while providing quality of services to the users. Advance communication is based on multiple switched...
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ISBN:
(纸本)9783319746906;9783319746890
Modern communication networks offer advance and diverse applications, which require huge usage of network resources while providing quality of services to the users. Advance communication is based on multiple switched networks that cannot be handle by traditional IP (internet protocol) networks. GMPLS (Generalized multiprotocol label switched) networks, an advance version of MPLS (multiprotocol label switched networks), are introduced for multiple switched networks. Traffic engineering in GMPLS networks ensures traffic movement on multiple paths. Optimal path(s) computation can be dependent on multiple objectives with multiple constraints. From optimization prospective, it is an NP (non-deterministic polynomial-time) hard optimization problem, to compute optimal paths based on multiple objectives having multiple constraints. The paper proposed a metaheuristic Pareto based bat algorithm, which uses two objective functions;routing costs and load balancing costs to compute the optimal path(s) as an optimal solution for traffic engineering in MPLS/GMPLS networks. The proposed algorithm has implemented on different number of nodes in MPLS/GMPLS networks, to analysis the algorithm performance.
The standard bat algorithm is slow convergence and low precision. To overcome this shortcoming, we propose a novel variant of bat optimization algorithm based on centroid strategy. The proposed algorithm has a better ...
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ISBN:
(纸本)9789811075216;9789811075209
The standard bat algorithm is slow convergence and low precision. To overcome this shortcoming, we propose a novel variant of bat optimization algorithm based on centroid strategy. The proposed algorithm has a better global searching capability because the centroid strategy can effectively prevent it falling into a local optimum. Two typical test functions are employed to test its performance. Simulation results show our proposal is both effective and efficient than three other comparison algorithms. Moreover, for high-dimensional function optimization, our proposed algorithm also has excellent approximation performance.
This paper represents, a new heuristic algorithm, bat algorithm, is used to optimize filter coefficients in the design of low pass finite impulse response (FIR) digital filter. The FIR digital filters have a wide rang...
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ISBN:
(纸本)9781538615010
This paper represents, a new heuristic algorithm, bat algorithm, is used to optimize filter coefficients in the design of low pass finite impulse response (FIR) digital filter. The FIR digital filters have a wide range of applications due to the features. Bounded input bounded output (BIBO) guarantees stability and is designed to provide linear phase at all frequencies. The bat algorithm is based on the local echolocation behaviors in the nature of the bats, and many areas such as system modeling, image processing and microwave are used. bat algorithm was used in the design of low pass FIR digital filters for different filter degrees and the obtained results were evaluated in performance analysis.
One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consu...
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One of the major challenges and difficulties to generate optimal operation rule for dam and reservoir operation are how efficient the optimization algorithm to search for the global optimal solution and the time-consume for convergence. Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. However, within the EA, there is a need to assume internal parameters at the initial stage of the model development, such assumption might increase the ambiguity of the model outputs. This study proposes a new hybrid optimization algorithm based on a bat algorithm (BA) and particle swarm optimization algorithm (PSOA) called the hybrid bat-swarm algorithm (HB-SA). The main idea behind this hybridization is to improve the BA by using the PSOA in parallel to replace the suboptimal solution generated by the BA. The solutions effectively speed up the convergence procedure and avoid the trapping in local optima caused by using the BA. The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. The results showed that the proposed HB-SA algorithm can achieve minimum irrigation deficits during the examined period and outperforms the other optimization algorithms. In addition, the computational time for the convergence procedure is reduced using the HB-SA. The proposed HB-SA is successfully examined and can be generalized for several dams and reservoir systems around the world.
Training of feed-forward neural networks is a well-known and a vital optimization problem which is used to digital image lossy compression. Since the inter-pixel relationship in the picture is highly non-linear and un...
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ISBN:
(纸本)9783319734415;9783319734408
Training of feed-forward neural networks is a well-known and a vital optimization problem which is used to digital image lossy compression. Since the inter-pixel relationship in the picture is highly non-linear and unpredictive in the absence of a prior knowledge of the picture itself, it has shown that the neural networks combined with metaheuristics can be very efficient optimization method for image compression issues. In this paper, we propose an improved bat algorithm for training the input-output weights of the network which contains input-output layers of the equal sizes and a hidden layer of smaller size in-between. It has applied on five standard digital images. From the experimental analysis, it can be shown that the proposed method produces an acceptable quality of the compressed image as well as a good ratio of compression.
As the bat algorithm (BA) has defects such as slow convergence and poor calculation precision, it is likely to result in local extremum, and Powell algorithm (PA) is sensitive to the initial value. To resolve the abov...
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ISBN:
(纸本)9783319959573;9783319959566
As the bat algorithm (BA) has defects such as slow convergence and poor calculation precision, it is likely to result in local extremum, and Powell algorithm (PA) is sensitive to the initial value. To resolve the above defects, advantages and disadvantages of PA and bat algorithm are combined in this paper to solve nonlinear equations. The hybrid Powell bat algorithm (PBA) not only has strong overall search ability like bat algorithm, but also has fine local search ability like Powell algorithm. Experimental results show that the hybrid algorithm can be used to calculate solutions to various nonlinear equations with high precision and fast convergence. Thus, it can be considered a positive method to solve nonlinear equations.
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