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FOA: fireworks optimization algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33151-33170页

作者： Ehsaeyan, Ehsan zolghadrasli, alireza Sirjan Univ Technol Elect Engn Dept Sirjan Iran Shiraz Univ Dept Commun & Elect Engn Shiraz Iran

In this paper, a novel meta-heuristic algorithm called Fireworks Optimization Algorithm (FOA) is introduced with few control parameters for discrete and continuous optimization problems. This algorithm is inspired from explosion pyrotechnic devices producing colorful spikes like red, blue and silver. By modelling the explosion behavior of the Fireworks in the sky, the search space can be swept efficiently to find the global optima. To improve the balance between the exploration and exploitation of individuals, three categories are defined to avoid local optimal traps and applied to the search agents. Each category has a different task and predefined updating position rules. A grouping strategy is considered to prevent the algorithm from premature convergence. The performance of FOA is demonstrated over 15 standard benchmarks in the continuous version and 30 images thresholding problems in the discrete version. The obtained results reveal the superiority of the proposed algorithm with fewer input parameters over other state-of-the-art optimization methods in most cases.

关键词： Fireworks optimization algorithm Bio-inspired algorithm Benchmarked functions meta-heuristic technique Image segmentation Engineering applications

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Classification of autism spectrum disorders individuals and controls using phase and envelope features from resting-state fMRI data

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2022年第1期10卷 55-66页

作者： Naghashzadeh, Mahshid Yazdi, Mehran zolghadrasli, alireza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

This study uses phase and envelope features extracted from fMRI data for discriminating between Autism Spectrum Disorders (ASD) and control subjects. We exploit the transfer function perturbation (TFP) method to estimate the instantaneous phase and envelope of intrinsic resting-state brain network components from fMRI data. Then we calculated power, entropy, and coherency features. We examined three different classifiers and two different feature selection algorithms, in a way that the subsets of features that best predict classes were selected using a sequential forward feature selection (SFFS) algorithm and principal component analysis (PCA). Afterwards, three different categories of calculated features, including phase features, envelope features, and a combination of phase and envelope features, fed into non-linear Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), and Deep Neural Network (DNN). Results illustrate that phase features are significantly discriminative and considerably improve ADSs and control subjects' classification accuracy and lead to robust prediction. Moreover, 91% of classification accuracy was obtained when the dimension of phase features was reduced by PCA and fed to the non-linear SVM. Eventually, the two-sample t-test and Pearson's correlation coefficient illustrated that phase features had a significantly lower correlation than envelope features.

关键词： Resting-state fMRI ASD instantaneous phase and envelope classification features brain connectivity

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Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method

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JOURNAL OF DIGITAL IMAGING 2021年第5期34卷 1209-1224页

作者： Saeidifar, Mahtab Yazdi, Mehran zolghadrasli, alireza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods.

关键词： Tumor detection Morphological operators Evolutionary algorithms K-means Otsu thresholding algorithm Active contour

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A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第1期22卷 p1-39页

作者： Ehsaeyan, Ehsan zolghadrasli, alireza Shiraz Univ Sch Elect & Comp Engn Dept Commun & Elect Engn Shiraz *** Iran

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

关键词： Image segmentation multilevel thresholding Darwinian theory Crow Search Algorithm energy curve swarm intelligence

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Context-oriented performance evaluation of network selection algorithms in 5G heterogeneous networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2022年 202卷

作者： Honarvar, Reza zolghadrasli, alireza Monemi, Mehdi Shiraz Univ Sch Elect & Comp Engn Shiraz Iran Salman Farsi Univ Kazerun Dept Elect & Comp Engn Kazerun Iran

Vertical handover is the essential feature of the next generation of heterogeneous wireless networks. Network selection among accessible candidates is the most critical step in vertical handover procedure. Although various algorithms for network selection have been proposed during recent years, there is no comprehensive study to consider all subscribers' context and requirements in a systematic structured manner. Furthermore, due to different approaches and variety of parameters related to this complicated problem, no inclusive benchmark exists to evaluate the performance of the algorithms. In this paper, we define a global user satisfaction indicator (USI) as a quantitative metric which comprehensively qualifies all candidate networks based on all subscribers' contexts. Our method is inspired by analytical hierarchical process (AHP) and utility function theory which systematically integrates both customer and network side parameters. This idea leads to proposing a novel algorithm called opportune context-aware network selection (OCANS). OCANS is a network-assisted user centric approach which dynamically and automatically takes into account customer context in the decision of the best network to achieve maximum user satisfaction. In addition to analytic computations, our practical simulation framework evaluates and compares OCANS to well-known network selection algorithms such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS) and Multiplicative Exponential Weighting (MEW). Simulation results along with analytic computations show that our proposed algorithm not only leads to higher USI, but also is aligned to customer preferences and has superior performance in desired indicators such as number of handovers, security grade, quality of service (QoS), energy consumption and service cost.

关键词： Vertical handover Network selection 5G heterogeneous network User satisfaction indicator Utility theory

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A Darwinian Differential Evolution Algorithm for Multilevel Image Thresholding

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INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 2021年第4期18卷

作者： Ehsaeyan, Ehsan zolghadrasli, alireza Shiraz Univ Sch Elect & Comp Engn Shiraz *** Iran

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods.

关键词： Image segmentation multilevel thresholding differential evolution algorithm Darwinian theory

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Multilevel Image Thresholding Based on Improved Expectation Maximization (EM) and Differential Evolution Algorithm

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INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 2021年第4期18卷

作者： Ehsaeyan, Ehsan zolghadrasli, alireza Shiraz Univ Sch Elect & Comp Engn Dept Commun & Elect Engn Shiraz *** Iran

Multilevel image thresholding is an essential step in the image segmentation process. Expectation Maximization (EM) is a powerful technique to find thresholds but is sensitive to the initial points. Differential Evolution (DE) is a robust metaheuristic algorithm that can find thresholds rapidly. However, it may be trapped in the local optimums and premature convergence occurs. In this paper, we incorporate EM algorithm to DE and introduce a novel algorithm called EM+DE which overcomes these shortages and can segment images better than EM and DE algorithms. In the proposed method, EM estimates Gaussian Mixture Model (GMM) coefficients of the histogram and DE tries to provide good volunteer solutions to EM algorithm when EM converges in local areas. Finally, DE fits GMM parameters based on Root Mean Square Error (RMSE) to reach the fittest curve. Ten standard test images and six famous metaheuristic algorithms are considered and result on global fitness. PSNR, SSIM, FSIM criteria and the computational time are given. The experimental results prove that the proposed algorithm outperforms the EM and DE as well as EM+ other natural-inspired algorithms in terms of segmentation criteria.

关键词： Image segmentation multilevel thresholding expectation maximization differential evolution Gaussian mixture model

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A Multilevel Image Thresholding Method Using the Darwinian Cuckoo Search Algorithm

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2021年第4期21卷

作者： Ehsaeyan, Ehsan zolghadrasli, alireza Shiraz Univ Sch Elect & Comp Engn Dept Commun & Elect Engn Shiraz Iran

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods.

关键词： Image segmentation multilevel thresholding Cuckoo search algorithm Darwinian theory

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Performance analysis of multihop decode-and-forward relay networks with diversity in Nakagami fading channels

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2020年 113卷 152973-000页

作者： Ghofrani-Jahromi, Salman zolghadrasli, alireza Neinavaie, Mohammad Shiraz Univ Dept Elect & Commun Engn Shiraz Iran

In this paper, we consider a multihop Decode-and-Forward (DF) relay network, in which each node receives the signals from its two preceding nodes. We assume Nakagami fading channels with integer fading parameters. In this network, the achievable diversity order is limited by the link between the source and the first relay. We derive the exact outage probability of fixed and selection relaying with spatial channel reuse, and demonstrate that applying selection relaying at the first relay is adequate to achieve full diversity. In order to derive the outage probability of this system in a Nakagami fading environment with integer fading parameters, a simple closed-form expression for the cumulative distribution function (CDF) of the sum of two independent, but not necessarily identically distributed, Erlang random variables is obtained. By approximating the outage probability for the high signal-to-noise ratio (SNR) region, we also derive the diversity order and coding gain of this selection relaying method and the fixed relaying method. The obtained asymptotic performance expressions are generalized for Nakagami fading channels with arbitrary real-valued fading parameters. Numerical and Monte Carlo results are provided which confirm the analytical expressions. (C) 2019 Elsevier GmbH. All rights reserved.

关键词： Selection relaying Multi-hop networks Decode-and-forward (DF) Outage probability Diversity order

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Mobility-Based Cell and Resource Allocation for Heterogeneous Ultra-Dense Cellular Networks

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IEEE ACCESS 2018年 6卷 66940-66953页

作者： Farokhi, Masoud zolghadrasli, alireza Yamchi, Nader Mokari Shiraz Univ Fac Elect & Comp Engn Shiraz *** Iran Tarbiat Modares Univ Dept Elect & Comp Engn Tehran 14115111 Iran

Heterogeneous ultra-dense network (HUDN) is a promising network structure, which increases network efficiency in 4G and 5G networks. However, it faces the new challenges regarding interference and mobility management. To overcome these challenges, joint of resource allocation (RA) and mobility management is necessary, which, to the best of our knowledge, has not been sufficiently investigated. This paper represents a solution to this issue. First, analytical investigation and numerical analysis are carried out to model and justify the behavior of expectation of handover (HO) success rate (E-R{HSR}) versus coverage probability. This paper provides more insight and tools for mobility-based RA researches and design of the network as well. Then, a new approach of hybrid cell-resource allocation is introduced. It is noteworthy that this is a practical structure that is adaptable to dynamic network changes in parameters, such as traffic distribution, mobility pattern, network topology, and different tiers' acceptable signal-to-interference-plus-noise ratio. The advantage of this new proposed approach is demonstrated by a numerical analysis. The results are compared with traditional approaches with and without HO priority consideration called hybrid-partial CRA (HP-CRA) and traditional CRA (T-CRA), respectively. The results show a considerable improvement of E-R{HSR} about 20% and 80% compared with HP-CRA and T-CRA, respectively, under the loaded situations, while the network sum rate is kept near the optimal solution.

关键词： Hybrid cell allocation and resource allocation mobility management stochastic geometry heterogeneous ultra-dense network handover success rate network sum rate

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