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Hybrid computational intelligence algorithms and their applications to detect food quality

ARTIFICIAL INTELLIGENCE REVIEW 2020年第2期53卷 1415-1440页

作者： Goel, Lavika Raman, Sundaresan Dora, Subham Swastik Bhutani, Anirudh Aditya, A. S. Mehta, Abhinav Birla Inst Technol & Sci Dept Comp Sci & Informat Syst Pilani 333031 Rajasthan India

Food security is a major problem faced today. With primitive storage facilities, especially in developing countries, it often leads to extensive losses. This work aims to develop algorithms based on vision data to assess the food quality and deploy them in food storage facilities to detect early signs of spoilage. This paper presents various segmentation techniques for finding spoilt food. Novel optimization techniques have been developed and implemented to improve K-means clustering and multilevel thresholding. A hybrid of moth flame optimization (MFO) and gravitational search algorithm (GSA) has been developed. Also, in another hybrid, particle swarm optimization (PSO) was also incorporated along with MFO and GSA. Both the hybrids performed better than the individual algorithms and the MFO-GSA-PSO hybrid performed better than the MFO-GSA hybrid on the benchmark functions. Segmented images using optimized K-means were used for feature extraction using local binary patterns (LBP). Multiclass support vector machine was used for classification which gave an accuracy of 81% for features from segmented images obtained using MFO-GSA hybrid and 83.33% for that using MFO-GSA-PSO hybrid. Results of simple linear iterative clustering superpixels for segmentation have also been discussed. The segmented clusters are then used to judge the rottenness of the food. Classification using LBP and Haralick features of the segmented image obtained using graphs over superpixels gave an accuracy of 81.7% and 78% respectively.

关键词： Moth flame optimization gravitational search algorithm Particle swarm optimization Simple linear iterative clustering (SLIC) superpixels K-means clustering Multilevel clustering Support vector machines

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A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine

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KNOWLEDGE-BASED SYSTEMS 2020年 195卷 105648-105648页

作者： Lv, Lu Wang, Wenhai Zhang, Zeyin Liu, Xinggao Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

Intrusion detection is a challenging technology in the area of cyberspace security for protecting a system from malicious attacks. A novel accurate and effective misuse intrusion detection system that relies on specific attack signatures to distinguish between normal and malicious activities is therefore presented to detect various attacks based on an extreme learning machine with a hybrid kernel function (HKELM). First, the derivation and proof of the proposed hybrid kernel are given. A combination of the gravitational search algorithm (GSA) and differential evolution (DE) algorithm is employed to optimize the parameters of HKELM, which improves its global and local optimization abilities during prediction attacks. In addition, the kernel principal component analysis (KPCA) algorithm is introduced for dimensionality reduction and feature extraction of the intrusion detection data. Then, a novel intrusion detection approach, KPCA-DEGSA-HKELM, is obtained. The proposed approach is eventually applied to the classic benchmark KDD99 dataset, the real modern UNSW-NB15 dataset and the industrial intrusion detection dataset from the Tennessee Eastman process. The numerical results validate both the high accuracy and the time-saving benefit of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Intrusion detection system Extreme learning machine gravitational search algorithm Differential evolution Kernel principal component analysis

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Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2020年第8期234卷 1175-1188页

作者： Deshamukhya, Tuhin Bhanja, Dipankar Nath, Sujit Natl Inst Technol Silchar Dept Mech Engn Silchar 788010 Assam India

In this novel optimization analysis, a rectangular porous fin subjected to convective tip conditions along with insulated end condition is studied. The aim is to optimize the important variables responsible for transferring heat from fin with insulated as well as convective tip to the surrounding in order to get a higher rate of heat transfer from the fin surface. Temperature-dependent solid and fluid thermal conductivities as well as variable internal heat generation is considered. To obtain heat dissipation rate through porous fin, the nonlinear governing equation is first solved analytically using Adomian decomposition method. Then the heat transfer rate, i.e., objective function of this problem is optimized with three powerful metaheuristic techniques, namely firefly algorithm, particle swarm optimization and gravitational search algorithm. A comparative analysis has revealed that fins with convective end show significantly higher heat transfer rate compared to their insulated end counterparts. Particle swarm optimization showed marginally higher heat transfer values compared to firefly algorithm but firefly algorithm took less computational effort to reach the optimum value.

关键词： Particle swarm optimization firefly algorithm gravitational search algorithm porous fin heat transfer

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Evolutionary tuning of filters coefficients for binaural audio equalization

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APPLIED ACOUSTICS 2020年第0期163卷 107204-000页

作者： Pepe, Giovanni Gabrielli, Leonardo Squartini, Stefano Cattani, Luca Univ Politecn Marche Dept Informat Engn Via Brecce Bianche I-60131 Ancona AN Italy ASK Ind SpA Viale Ramazzini I-42124 Reggio Emilia RE Italy

This paper presents a novel multichannel audio equalization technique based on evolutionary computation algorithms for tuning the filters coefficients. Specifically, two distinct evolutionary algorithms are used on purpose, i.e. the Particle Swarm Optimization (PSO) and gravitational search algorithm (GSA). Two alternative solutions for the definition of evolutionary particles have been devised and tested with both techniques. Given the desired frequency response, the fitness function is formulated in terms of amplitude spectral distance. These techniques have been assessed by computer experiments, conducted on a in-car binaural equalization scenario considering 7 loudspeakers and a binaural microphone. The obtained results show that the proposed solutions achieve a remarkably superior performance compared to the baseline methods, with a 5 times reduction of the mean square error in the amplitude spectral domain. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation FIR filter design Audio equalization Automotive audio Particle swarm optimization gravitational search algorithm

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Optimal probabilistic reliable hybrid allocation for system reconfiguration applying WT/PV and reclosures

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AIN SHAMS ENGINEERING JOURNAL 2020年第1期11卷 109-118页

作者： Swief, R. A. El-Amary, Noha H. Ain Shams Univ Fac Engn Cairo 11566 Egypt AASTMT Cairo 2033 Egypt

This paper introduces an optimal probabilistic study based on multi objective problem integrating the stochastic behavior of the renewable resources to improve system performance properties which are represented in reducing Energy Not Served index and reducing the system power loss. The reliability indices in power system have been effected applying reconfigurations, inserting reclosures and integrating distributed generators and setting the optimal location with the appropriate size of the allocated distributed generators. The aim of the study is hugely altered according to the appropriate allocation of the reclousers and DGs with the optimal choice for the DG sizes. The achievement of the proposed methodology is to solve the technical problems while keeping distribution network with minimum pollution, so distributed generators are preferable to be renewable sources such as wind and PV energies. The volatility nature of the renewable sources, wind & PV units, makes the study crucial to embrace the probabilistic behavior of those sources. Due to the complexity of the problem which is vastly increased with the increase of the factors affecting the study, two Meta heuristic techniques are competing each other to reach the optimal solution with the optimal technique. The approached algorithm under study is examined utilizing IEEE 69 modified system. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Reconfiguration Reliability Probabilistic performance Wind PV Whale optimization algorithm gravitational search algorithm

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Fuzzy mutation embedded hybrids of gravitational search and Particle Swarm Optimization methods for engineering design problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 95卷 103847-103847页

作者： Kar, Devroop Ghosh, Manosij Guha, Ritam Sarkar, Ram Garcia-Hernandez, Laura Abraham, Ajith Jadavpur Univ Comp Sci & Engn Dept 188Raja SC Mallick Rd Kolkata 700032 W Bengal India Univ Cordoba Area Project Engn Cordoba Spain Machine Intelligence Res Labs MIR Labs Sci Network Innovat & Res Excellence Auburn WA 98071 USA

gravitational search algorithm (GSA) and Particle Swarm Optimization (PSO) are nature-inspired, swarm-based optimization algorithms respectively. Though they have been widely used for single-objective optimization since their inception, they suffer from premature convergence. Even though the hybrids of GSA and PSO perform much better, the problem remains. Hence, to solve this issue, we have proposed a fuzzy mutation model for two hybrid versions of PSO and GSA - gravitational Particle Swarm (GPS) and PSOGSA. The developed algorithms are called Mutation based GPS (MGPS) and Mutation based PSOGSA (MPSOGSA). The mutation operator is based on a fuzzy model where the probability of mutation has been calculated based on the closeness of particle to population centroid and improvement in the particle value. We have evaluated these two new algorithms on 23 benchmark functions of three categories (unimodal, multimodal and multimodal with fixed dimension). The experimental outcome shows that our proposed model outperforms their corresponding ancestors, MGPS outperforms GPS 13 out of 23 times (56.52%) and MPSOGSA outperforms PSOGSA 17 times out of 23 (73.91%). We have also compared our results against those of some recently proposed optimization algorithms such as Sine Cosine algorithm (SCA), Opposition-Based SCA, and Volleyball Premier League algorithm (VPL). In addition, we have applied our proposed algorithms on some classic engineering design problems and the outcomes are satisfactory. The related codes of the proposed algorithms can be found in this link: Fuzzy-Mutation-Embedded-Hybrids-of-GSA-and-PSO.

关键词： Fuzzy logic Mutation gravitational search algorithm Particle swarm optimization Hybrid Engineering design

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An Efficient Automatic Intrusion Detection in Cloud Using Optimized Fuzzy Inference System

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2020年第4期14卷 22-41页

作者： Shyla, S. Immaculate Sujatha, S. S. Nagercoil Manonmaniam Sundaranar Univ Dept Comp Sci ST Hindu Coll Nagercoil India Nagercoil Manonmaniam Sundaranar Univ Dept Comp Sci & Applicat ST Hindu Coll Nagercoil India

Security incidents such as denial of service (DoS), scanning, malware code injection, viruses, worms, and password cracking are becoming common in a cloud environment that affects companies and may produce a financial loss if not detected in time. Such problems are handled by presenting an intrusion detection system (IDS) into the cloud. The existing cloud IDSs affect low detection accuracy, high false detection rate, and execution time. To overcome this problem, in this article, a gravitational search algorithm-based fuzzy inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely;possibilistic fuzzy c-means (PFCM) based clustering, training based on the GSA-FIS, and testing process. Initially, the incoming data is pre-processed and clustered with the help of PFCM. PFCM detects the noise of fuzzy c-means clustering (FCM), then conquers the coincident cluster problem of possibilistic fuzzy c-means (PCM) and eradicate the row sum constraints of fuzzy possibilistic c-means clustering (FPCM). After the clustering process, the clustered data is given to the optimized fuzzy inference system (OFIS). Here, normal and abnormal data are identified by the fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely-probe, remote to local (R2L), user to root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different schemes in terms of precision, recall and F-measure.

关键词： Cloud Computing DOS Fuzzy Inference System gravitational search algorithm Intrusion Detection System Possibilistic Fuzzy C-Means Probe R2L

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A chaotic digital secure communication based on a modified gravitational search algorithm filter

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INFORMATION SCIENCES 2012年 208卷 14-27页

作者： Han, XiaoHong Chang, XiaoMing Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Software Taiyuan 030024 Shanxi Peoples R China

Research in recent years has seen the development of chaotic systems for secure communication. However, most chaotic systems fail to compensate for channel noise which often degrades the performance of chaos-based secure communication systems. In this work, we propose a chaotic secure communication scheme based on the Modified gravitational search algorithm (MGSA), which minimizes premature convergence of gravitational search algorithm (GSA). Here, we apply the MGSA-based filter to the proposed communication scheme to reduce channel noise. Computer simulations with the unified chaotic map are done to verify the feasibility of the proposed secure communication scheme. The results show that the proposed new scheme accurately estimates the states and information symbols, and provides a lower bit error rate (BER) than existing secure communication schemes. Furthermore, the MGSA is tested on the nonlinear filter modeling and compared with GSA and particle swarm optimization (PSO). The results confirm the high performance of the MGSA-based filter in parameters estimation of nonlinear filter modeling. In other words, the more accurately the MGSA estimates the parameters, the more noise the filter reduces. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Secure communication Message estimation gravitational search algorithm Chaos

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Chaotic secure communication based on a gravitational search algorithm filter

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第4期25卷 766-774页

作者： Han, XiaoHong Chang, XiaoMing Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Software Taiyuan 030024 Shanxi Peoples R China

A new chaotic secure communication scheme based on a gravitational search algorithm (GSA) filter is proposed. In this scheme, useful signals are delivered via an encoder, a chaotic transmitter, a GSA-based filter, a chaotic receiver, and a decoder. The security of such a communication system is promoted due to the unpredictable features of the chaotic map and the unknown encoding-modulation scheme. By using a GSA filter technique the resistance of the system to noise is enhanced. To verify the effectiveness of the proposed scheme, it is compared with the current state-of-the-art schemes in simulations. At the same time, comparisons with a genetic algorithm (GA) filter and a particle swarm optimization (PSO) filter are made. Numerical simulations confirm that the proposed method is better in estimating the states and information symbols, and has a lower bit error rate than other schemes. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Secure communication Message estimation gravitational search algorithm Chaos

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Optimal power flow using gravitational search algorithm

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ENERGY CONVERSION AND MANAGEMENT 2012年 59卷 86-95页

作者： Duman, Serhat Guvenc, Ugur Sonmez, Yusuf Yorukeren, Nuran Duzce Univ Tech Educ Fac Dept Elect Educ Duzce Turkey Gazi Univ Gazi Vocat Coll Dept Elect Technol Ankara Turkey Kocaeli Univ Fac Engn Dept Elect Engn Kocaeli Turkey

In this paper, gravitational search algorithm (GSA) is proposed to find the optimal solution for optimal power flow (OPF) problem in a power system. The proposed approach is applied to determine the optimal settings of control variables of the OPF problem. The performance of the proposed approach examined and tested on the standard IEEE 30-bus and 57-bus test systems with different objective functions and is compared to other heuristic methods reported in the literature recently. Simulation results obtained from the proposed GSA approach indicate that GSA provides effective and robust high-quality solution for the OPF problem. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow gravitational search algorithm Optimization Power systems

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