Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth's surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as m...
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Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth's surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as maximum a posteriori estimation estimations of Markov random field (MRF). The key issue of this formulation is energy minimization. Iterated conditional mode (ICM), graph cuts (GC), loopy belief propagation (LBP), and sequential tree-reweighted message passing (TRW-S) have been proposed for the energy minimization. Unfortunately, they differ in the formulation of the MRF model for PU, which raises the question of how they compare against each other on the same MRF model for PU. We address this by investigating the four optimization algorithms and comparing them on an identical MRF model, which gives researchers some guidance as to which optimization method is best suited for solving the PU problem. Experiments using simulated and real-data illustrate that the GC algorithm is clearly the winner among the four algorithms in all cases. The ICM algorithm, although very rapid, performs much worse than the other three especially in the terrain with violent changes or discontinuities. The two message-passing algorithms-LBP and TRW-S-perform completely differently. The LBP algorithm performs surprisingly poorly on solving phase discontinuities issue, whereas the TRW-S algorithm does quite well (second only to the GC algorithm). (C) The Authors.
A novel optimization algorithm, called the Magnetic optimization algorithms (MOAs), is proposed in this paper which is inspired by the principles of magnetic field theory. In MOA, the possible solutions are some magne...
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A novel optimization algorithm, called the Magnetic optimization algorithms (MOAs), is proposed in this paper which is inspired by the principles of magnetic field theory. In MOA, the possible solutions are some magnetic particles scattered in the search space. In this respect, each magnetic particle has a measure of mass and magnetic field according to its fitness. In this scheme, the fitter magnetic particles are more massive, with stronger magnetic field. In terms of interaction, these particles are located in a structured population and apply a long range force of attraction to their neighbors. Ten different structures are proposed for the algorithm and the structure that offers the best performance is found. Also, to improve the exploration ability of the algorithm, several operators are proposed: a repulsive short-range force, an explosion operator, a combination of short-range force and explosion operator and a crossover interaction between the neighboring particles. In order to test the proposed algorithm and the proposed operators, the algorithm is compared with a variety of existing algorithms on 21 numerical benchmark functions. The experimental results suggest that the proposed algorithm outperforms some of the existing algorithms. (C) 2014 Elsevier B.V. All rights reserved.
Due to the importance of beamforming in improving the communication systems performance, this paper presents a novel study of beamforming of planar antenna arrays (PAAs) utilizing the Improved Grey Wolf optimization (...
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Due to the importance of beamforming in improving the communication systems performance, this paper presents a novel study of beamforming of planar antenna arrays (PAAs) utilizing the Improved Grey Wolf optimization (I-GWO) algorithm with the goal of minimizing the peak sidelobe level (PSLL). It is very important to suppress the sidelobe level (SLL) because it minimizes interference and received noise. A two-dimensional (2D) optimization method is presented to find the optimal amplitude excitations and element placements in PAA. The effectiveness of beamforming optimization using the I-GWO is illustrated by comparing it with different metaheuristic algorithms such as Particle Swarm optimization (PSO), Gravitational Search Algorithm (GSA), Hybrid Particle Swarm optimization with Gravitational Search Algorithm (PSOGSA), Runge Kutta Optimizer (RUN), Slime Mould Algorithm (SMA), Harris Hawks optimization (HHO), as well as the original Grey Wolf Optimizer (GWO). Simulation findings show that antenna array beamforming using I-GWO is effective using the 2D optimization method compared to the other algorithms, where the 2D technique achieved the most decreased SLL with the fewest array elements, which helps reduce the cost of the entire system. This clearly shows that I-GWO is very efficient and can be applied to solve different beamforming optimization problems. It can also be used for the radiation pattern synthesis of other antenna array geometries for different wireless networks applications.
As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has...
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As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.
Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capab...
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Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capability of algorithms in optimization, but not all of them could be optimized, because they were different from each other in dimensionality, separability, scalability, and modality ***.. In this paper, we introduced another property called symmetry or non-symmetry, which should be another embedded characteristic of functions affecting the capability of algorithms in optimization. 67 non-symmetric benchmark functions were collected and 9 popular capability-verified algorithms were introduced in four types of simulation experiments. Experimental results show that most of the non-symmetric algorithms could not be optimized. And none of the algorithms involved could optimize them all. Efforts remain in need of new methods and improvements of nature-inspired algorithms.
This paper discusses how social network theory can provide optimization algorithms with social heuristics. The foundations of this approach were used in the SAnt-Q (Social Ant-Q) algorithm, which combines theory from ...
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This paper discusses how social network theory can provide optimization algorithms with social heuristics. The foundations of this approach were used in the SAnt-Q (Social Ant-Q) algorithm, which combines theory from different fields to build social structures for state-space search, in terms of the ways that interactions between states occur and reinforcements are generated. Social measures are therefore used as a heuristic to guide exploration and approximation processes. Trial and error optimization techniques are based on reinforcements and are often used to improve behavior and coordination between individuals in a multi-agent system, although without guarantees of convergence in the short term. Experiments show that identifying different social behavior within the social structure that incorporates patterns of occurrence between states explored helps to improve ant coordination and optimization process within Ant-Q and SAnt-Q giving better results that are statistically significant. (C) 2012 Elsevier Ltd. All rights reserved.
The evolution of metaheuristic optimization algorithms towards identification of a global minimum is based on random numbers, making each run unique. Comparing the performance of different algorithms hence requires se...
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The evolution of metaheuristic optimization algorithms towards identification of a global minimum is based on random numbers, making each run unique. Comparing the performance of different algorithms hence requires several runs, and some statistical metric of the results. Mean, standard deviation, best and worst values metrics have been used with this purpose. In this paper, a single probabilistic metric is proposed for comparing metaheuristic optimization algorithms. It is based on the idea of population interference, and yields the probability that a given algorithm produces a smaller (global?) minimum than an alternative algorithm, in a single run. Three benchmark example problems and four optimization algorithms are employed to demonstrate that the proposed metric is better than usual statistics such as mean, standard deviation, best and worst values obtained over several runs. The proposed metric actually quantifies how much better a given algorithm is, in comparison to an alternative algorithm. Statements about the superiority of an algorithm can also be made in consideration of the number of algorithm runs and the number of objective function evaluations allowed in each run. (C) 2017 Elsevier Ltd. All rights reserved.
From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the deve...
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From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.
Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground vibration is a practical need, especially for saf...
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Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground vibration is a practical need, especially for safety issues. This research proposes hybrid artificial intelligence schemes for predicting ground vibration. The approaches are based on support vector regression (SVR) optimized with firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO). Additionally, a hybrid FFA and artificial neural network (ANN) model and several well-known empirical models were also employed in this study. In the predictive modeling process, 90 sets of data, collected from two quarry mines in Iran, divided into two datasets, namely a training dataset and a testing dataset, were used. After model development, to provide an objective assessment of the predictive model performances, their results were compared based on several well-known and popular statistical criteria. FFA-SVR exhibits much more efficiency and reliability than PSO-SVR, GA-SVR, FFA-ANN models in terms of ground vibration prediction, indicating the superiority of FFA over PSO and GA in the SVR training.
After each blasting operation in surface mines, undesirable environmental impacts, such as air-blast (AB) and ground vibration, are inevitable. Therefore, minimizing and controlling these impacts are crucial in order ...
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After each blasting operation in surface mines, undesirable environmental impacts, such as air-blast (AB) and ground vibration, are inevitable. Therefore, minimizing and controlling these impacts are crucial in order to reduce environmental problems. This study presents new and practical advanced machine learning methods for AB prediction using 62 datasets gathered from four quarry sites in Malaysia. The developed models were constructed based on the boosted regression tree (BRT) and least-squares support vector machine (LSSVM), improved with three metaheuristic algorithms: the gray wolf optimizer (GWO), genetic algorithm (GA), and artificial bee colony (ABC). Six hybrid models, namely BRT-GA, BRT-ABC, BRT-GWO, LSSVM-GA, LSSVM-ABC, and LSSVM-GWO models, were developed and their performances were evaluated using metrics such as R-squared correlation and other methods like the Taylor diagram and quantile-quantile plots. To provide a better assessment of the models' performances, the dataset were categorized into training and testing parts. The results demonstrated that, among the six hybrid models, the LSSVM-GWO model provided the highest efficiency in the testing part while the BRT-GWO model had the best accuracy but the performance of BRT-GWO was the best in the training part. In other words, the BRT hybrid models had the best performance in training, and LSSVM hybrid models in the testing part. The results indicate the effectiveness of combining GWO with LSSVM and BRT models to predict AB.
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