A novel design and implementation technique for low complexity variable digital filters using multi-objective artificial bee colony optimization and a minimal spanning tree approach

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters’ optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective Artificial Bee Colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the e-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

<span>This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the proposed scheme more secure, the watermark is encrypted chaotically by logistic chaotic encryption before embedding it to the host (original) image. The experimental results show excellent imperceptibility and good resiliency against a wide range of image processing attacks.</span>

Our previous work has shown that a three-channel (3-channel) variable filter-bank (VFB) can be successfully applied for compensating various hearing loss patterns (audiograms) with high-accuracy. The 3-channel VFB consists of variable lowpass, variable bandpass, and variable highpass digital filters that are transformed from a normalized analog prototype Chebyshev type-I lowpass filter. Since both the magnitudes (gains) and band edge frequencies of the three variable digital filters are independently adjustable, the 3-channel VFB is considerably flexible for achieving high-accuracy audiogram fitting. The objective of this paper is to reveal and theoretically prove the numerator coefficient-symmetries of the three variable filters, and show that each variable filter requires only one multiplication for its numerator filtering operations, so the total number of multiplications can be significantly reduced, which facilitates high-speed audiogram fitting and low-power implementation.

Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) – common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less.

Artificial Bee Colony (ABC) optimization algorithm has captured much attention of researchers from various fields, in recent times. Moreover, various comparative studies clearly reports robust convergence of ABC algorithm than other bio-inspired optimization algorithms. Nevertheless, like other optimization algorithms, ABC suffers from slower convergence and tendency towards local optima trappings. Therefore, various amendments have been proposed to avertthe flaws of ABC algorithm. Nonetheless, the variants are either computationally intensive or could not avert the flaws of the algorithms. Hence, this research work proposes an efficient variant of ABC algorithm. The proposed variant capitalizes on the global-best food-source. The proposed variant has been compared with various existing variants of ABC algorithm on a few benchmark functions. Significance of the proposed variant has also been analyzed statistically. Results show the best convergence of the proposed variant among all the compared optimization algorithms on all benchmark functions.

In this paper, we define filters capable of changing frequency response as variable digital filters (VDFs). In the previous VDF having changed a piecewise large attenuation characteristic in the stopband, it is difficult to decrease the filter degree to two or more, changing noises. We propose a transfer function of FIR filters with changing some large attenuations in the stopband by using spectral parameters and its design method. The number of spectrum parameters used here uses the number of changing stopbands. In this method, the minimization problems are formulated as semidefinite programming (SDP) in the frequency domain. Moreover, we present that the proposed VDF can be implemented using Farrow structure. Finally, we confirm the effectiveness of this method through numerical examples.

Design of frequency response masking FIR filter in the Canonic Signed Digit space using modified Artificial Bee Colony algorithm

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

Recently, variable digital filters (VDFs) have become an essential research topic. This paper presents a simple method for the design of infinite impulse response (IIR) VDFs with high tuning accuracy. First, VDFs were created using a state-space model based on a frequency transformation, which requires the inverse matrix and the square root that leads to high computational cost. Then a new algorithm was implemented to generate the same VDFs absent the complex calculations that utilized negative binomial and Taylor series approximations in a simple state-space formulation. This algorithm provides high-accuracy approximations of the inverse matrix and square root compared to other algorithms that have been developed. Thus, the proposed VDFs have high tuning accuracy with respect to finite wordlength effects.

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .

This paper studies the design and multiplier-less realization of variable fractional delay matched filters (VFD-MFs), which provide matching filtering and variable fractional delay of the filter output. It offers greater flexibility and lower delay in symbol-timing adjustment than directly cascading a match filter with a fractional delayer. The design of VFD-MFs, which can be viewed as a variable digital filter (VDF) design problem subject to the matched filtering condition, is formulated as a second order cone programming (SOCP) problem with least square design criteria. The proposed VFD-MFs can be efficiently implemented using the Farrow structure. By employing sum-of-power-of-two (SOPOT) coefficients and the multiplier block (MB) technique, very efficient multiplier-less realization of the VFD-MF with low hardware complexity is obtained. A design example is given to demonstrate the effectiveness of the proposed approach.

Low complexity reconfigurable channelizers using non-uniform filter banks

Often, solutions to complex problems are found in nature. Swarm algorithms are capable of solving such complex problems by implementing patterns from nature. This patterns are found in a variety of scientific fields. In this paper, we discuss two swarm algorithms extracted from Biology and Physics, namely: Multiobjective Artificial Bee Colony (MOABC) and Multiobjective Gravitational Search Algorithm (MOGSA). The first one is based on bees behavior and the other follows the gravity between masses. These algorithms are implemented to solve the grid scheduling problem. Optimization of job scheduling is one of the most challenging tasks in Grid environments because it severely affects the execution time of an experiment (set of jobs). Experiments often are tied up to fulfill deadlines and budgets. One of the main contributions of this work is adding multiobjective processes to these swarm algorithms to minimize those conflictive objectives. Results show that MOABC clearly improves the MOGSA approach when solving the problem. MOABC is also compared with real grid meta-schedulers as Deadline Budget Constraint (DBC) and Workload Management System (WMS) by using the simulator GridSim to prove the improvement that offers this new algorithm.

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Artificial Bee Colony optimization algorithms. Comparing these algorithms on solving Track Backer-Upper problem –a reinforcement fuzzy control problem– shows that Artificial Bee Colony Optimization algorithm has the best efficiency in combining with fuzzy- Q Learning.

Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 × 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.