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6th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Ighravwe, Desmond Eseoghene Mashao, Daniel Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa

ISBN: (纸本)9781728145778

Several studies have called the attentions of utility firms to the possibility of using mathematical models to measure and monitor energy theft. Unfortunately, these studies have decoupled the contributions of government policies, such as social, technical and economic policies, from their evaluation process. To address this knowledge gaps, this article modelled energy theft using soft computing approach: fuzzy cognitive map (FCM) and swarm algorithm. Fuzzy logic was used to design cognitive maps for energy theft parameters;second, and swarm algorithm was used to determine the weights and concepts values. The practicality of the swarm-based model was tested using experts' judgements. This model performance was compared with evolutionary-based FCM and it was observed that it performed better than the evolutionary-based model. And when the swarm-based model performance was compared with experts' judgements, it performed satisfactorily.

关键词： swarm algorithm energy theft fuzzy cognitive maps

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VLSI Elements Placement Based on Simulation of Bats Behavior in Nature 10

VLSI Elements Placement Based on Simulation of Bats Behavior...

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10th IEEE International Conference on Application of Information and Communication Technologies (AICT)

作者： Kuliev, Elmar Lezhebokov, Andrey Kravchenko, Yuri Southern Fed Univ Dept CAD Rostov Na Donu Russia

ISBN: (纸本)9781509018413

The paper deals with NP-hard optimization problems with the use of behavioral foundations of subject in nature. swarm intelligent methods and algorithms are the most promising ones. Suggested algorithm is based on the simulation of bats' behavior. The authors concern a behavioral model of a bat - it moves within search space for the purpose of finding quasi-optimal solutions. Also there are represented approaches to adjust control parameters of the algorithm (frequency, amplitude, direction of movement). On the basis of bat's behavior a schematic diagram is developed. Initial search space are formed by sequential, random and iterative algorithms. To confirm theoretical estimations of time complexity and effectiveness of the developed algorithm computational experiments are carried out.

关键词： placement adaptive procedures evolutionary simulation swarm algorithm bats' behavior software

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Harris Hawks Optimisation: Using of an Archive 20th

Harris Hawks Optimisation: Using of an Archive

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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Bujok, Petr Univ Ostrava 30 Dubna 22 Ostrava 70200 Czech Republic

ISBN: (纸本)9783030879860;9783030879853

This paper proposes an enhanced variant of the novel and popular Harris Hawks Optimisation (HHO) method. The original HHO algorithm was studied in many research projects, and a lot of hybrid (cooperative) variants of HHO was proposed. In this research study, an advanced HHO algorithm with an archive of the old solutions is proposed (HHOA). The proposed method is experimentally compared with the original HHO algorithm on a set of 22 real-world problems (CEC 2011). The results illustrate the superiority of HHOA because it outperforms HHO significantly in 20 out of 22 problems, and it is never significantly worse. Four well-known nature-based algorithms were employed to compare the efficiency of the proposed algorithm. HHOA achieves the best results in overall statistical comparison. A more detailed comparison shows that HHOA achieves the best results in half real-world problems, and it is never the worst-performing method. A newly employed archive of old solutions significantly increases the performance of the original HHO algorithm.

关键词： swarm algorithm Harris Hawks Optimisation Real-world problems Archive Experimental comparison

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Virtual Machine Placement Strategy Research

Virtual Machine Placement Strategy Research

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5th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Gao, Jipeng Tang, Gaoming Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9780769551067

With the emergence of cloud computing, users begin to have more abstract concept for the physical machine. IaaS providers prohibits users request specify the physical machine to virtual machine resources. Users know nothing about underlying infrastructure resources. The data center carry out virtual machine placement decision-making. In this paper, we use particle swarm optimization theory to guide the placement of virtual machines in the data center resources modeling and analysis. According to user needs and current physical server load state, the algorithm quantifies each physical machine and the virtual machine's CPU uses, memory uses, and storage uses. By using multi-objective particle swarm optimization algorithm, the placement of virtual machines based on the physical server resource utilization and the number of virtual machine migration. Simulation results show that the algorithm can effectively improve the minimum number of virtual machine migration resource utilization.

关键词： virtual machines placement swarm algorithm resource utilization cloud computing

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Parallel simulated annealing using simplex method

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AIAA JOURNAL 2006年第12期44卷 3143-3146页

作者： Luo, Ya-Zhong Tang, Guo-Jin Natl Univ Def Technol Coll Aerosp & Mat Engn Changsha 410073 Peoples R China

An improved edition of hybrid algorithms combining simulated annealing (SA) with and simplex method (SM), a parallel simulated annealing using simplex method (PSASM), was proposed, and applied to structural optimization. The simplex can be regarded as a population including (n+1) individuals for n-diemsional optimization problem, The excellent individuals of the simplex can be spread out using reflection, expansion or contraction operation. The PSAMS was evaluated in two functional problems, which included a two-dimensional function and the Golinski's speed reducer problem. The results of the PSASM were found to be able to locate the near-optimal solution at probability 100%, with low computational cost. The PSAMS was also applied to a 10-bar truss structural design problem, and was found competitive when compared with other stochastic optimization algorithms. More implementations for the PSASM further examined are expected to improve its performance.

关键词： Annealing Structural Optimization Trusses Stochastic Optimization MDO Mathematical Models Genetic algorithm swarm algorithm NASA Langley

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Optimization of multistage depressed collectors using fem and parallel algorithm MeTEO

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第6期32卷 1955-1963页

作者： Coco, Salvatore Laudani, Antonino Pulcini, Giuseppe Fulginei, Francesco Riganti Salvini, Alessandro Univ Catania Dipartimento Ingn Elettr Elettron & Informat Catania Italy Univ RomaTre Dipartimento Elettron Applicata Rome Italy

Purpose - The paper aim is the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of Multistage Depressed Collector. Design/methodology/approach - The Flock-of-Starlings Optimization (FSO), the Particle swarm Optimization (PSO) and the Bacterial Chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO+BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search. Findings - The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort. Practical implications - The development of an efficient method for the solution of a complicated electromagnetic optimization problem, exploiting the different characteristic of different approaches based on evolutionary computation algorithm. Originality/value - The paper shows that the combination of stochastic methods having different exploration properties with appositely developed FE electromagnetic simulator allows us to produce effective solutions of multimodal electromagnetic optimization problems, with an acceptable computational cost.

关键词： Evolutionary computation swarm algorithm Flock of starlings optimization Traveling wave tubes Multistage depressed collectors Electrodes Optimization techniques Parallel algorithms Electrodes Solutions VOLTAGE MULTISTAGE Collectors Finite element method Feeling of Sadness traveling wave tubes evolutionary algorithm artificial life

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Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish

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BIOINSPIRATION & BIOMIMETICS 2023年第6期18卷 066007-066007页

作者： Connor, Jack Joordens, Matthew Champion, Benjamin Deakin Univ Sch Engn Geelong Vic 3216 Australia

This study aims to present a novel flocking algorithm for robotic fish that will aid the study of fish in their natural environment. The algorithm, fish-inspired robotic algorithm (FIRA), amalgamates the standard flocking behaviors of attraction, alignment, and repulsion, together with predator avoidance, foraging, general obstacle avoidance, and wandering. The novelty of the FIRA algorithm is the combination of predictive elements to counteract processing delays from sensors and the addition of memory. Furthermore, FIRA is specifically designed to work with an indirect communication method that leads to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. By leveraging a high-latency, non-guaranteed communication methodology inspired by stigmergy methods inherent in nature, FIRA successfully addresses some of the obstacles associated with underwater communication. This breakthrough enables the realization of inexpensive, multi-agent swarms while concurrently harnessing the advantages of tetherless communication. FIRA provides a computational light control algorithm for further research with low-cost, low-computing agents. Eventually, FIRA will be used to assimilate robots into a school of biological fish, to study or influence the school. This study endeavors to demonstrate the effectiveness of FIRA by simulating it using a digital twin of a bio-inspired robotic fish. The simulation incorporates the robot's motion and sensors in a realistic, real-time environment with the algorithm used to direct the movements of individual agents. The performance of FIRA was tested against other collective flocking algorithms to determine its effectiveness. From the experiments, it was determined that FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.

关键词： bio-inspired swarm algorithm swarm robotics robotic fish underwater

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Homonuclear Molecules Optimization (HMO) meta-heuristic algorithm

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KNOWLEDGE-BASED SYSTEMS 2022年 258卷

作者： Mahdavi-Meymand, Amin Zounemat-Kermani, Mohammad Polish Acad Sci Inst Hydroengn Warsaw Poland Shahid Bahonar Univ Kerman Water Engn Dept Kerman Iran

This study introduces a novel meta-heuristic algorithm known as Homonuclear Molecules Optimization (HMO) for optimizing complex and nonlinear problems. HMO is inspired by the arrangement of electrons around atoms given the Bohr atomic model and the structure of homonuclear molecules. This algorithm is based on creating the initial population of a set of atoms in the search space and the electrons associated with each atom (searching agents) given the quantum numbers. In each iteration, the best electron of each atom is selected as the new location of the nucleus, and a number of atoms move toward the atom with the best solution to form a homonuclear molecule. The results of applying the MHO algorithm were evaluated in comparison with three classical optimization algorithms of PSO, GA, and DE along with a novel algorithm called Equilibrium Optimizer (EO). The HMO was able to precisely solve unimodal functions and find global and local solutions for multimodal functions. The outcomes of Wilcoxon's rank-sum test demonstrated that there is a significant difference between the final results of HMO and those of the other algorithms (a = 0.05%). Overall, it was concluded that the HMO outperforms the classical algorithms and can compete with new and efficient algorithms such as EO.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Search algorithm Non-linear optimization Artificial intelligence swarm algorithm Electron cloud

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Enhancing CO2 emissions prediction in Africa: A novel approach integrating enviroeconomic factors and nature-inspired neural network in the presence of unit root

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RENEWABLE ENERGY 2024年 237卷

作者： Mati, Sagiru Baita, Abubakar Jamilu Goran Yousif, Ismael Abdullahi, Salisu Garba Samour, Ahmed Ozsahin, Dilber Uzun Near East Univ Operat Res Ctr Healthcare TRNC Mersin 10 TR-99138 Nicosia North Cyprus Turkiye Yusuf Maitama Sule Univ Dept Econ Kano Nigeria Univ Islam Int Indonesia Fac Econ & Business Depok Indonesia Noble Inst Technol Dept Business Adm Erbil Kurdistan Regio Iraq Coll Educ & Legal Studies Dept Econ Ringim Nigeria Dhofar Univ Dept Accounting Salalah Oman Univ Sharjah Coll Hlth Sci Dept Med Diagnost Imaging Sharjah U Arab Emirates Univ Sharjah Res Inst Med & Hlth Sci Sharjah U Arab Emirates

The prediction of CO2 emissions is critical for designing sustainable environmental policies and meeting the Sustainable Development Goals, particularly those related to climate action. Therefore, this paper aims to assess the appropriate predictive model for tracking carbon emissions in four African economies with the highest carbon emissions. The sample countries comprise Algeria, Egypt, Nigeria, and South Africa, spanning the period from 1990 to 2014. Artificial Neural Network coupled with Particle swarm Optimisation (ANNPSO) is compared with the Autoregressive model of order 1 (AR), Autoregressive Integrated Moving Average (ARIMA), and Extreme Learning Machine (ELM). Unit root tests are utilised to check the stationarity of the enviroeconomic variables. For the training sample, the ANN-PSO model increased the predictive accuracy of the AR model by 78.50%, 91.18%, 86.4%, and 86.58% for Algeria, Egypt, Nigeria, and South Africa, respectively. For the testing sample, the ANN-PSO model improved the performance of the benchmark model by 95.36%, 83.64%, 97.28%, and 83.03% for Algeria, Egypt, Nigeria, and South Africa, respectively. The evaluation criteria show that ANN-PSO is the most fitting model for predicting carbon emissions in the selected countries. The study concludes that the ANN-PSO model could be valuable for formulating futuristic climate policies to ensure environmental resilience.

关键词： Global warming swarm algorithm Nonlinear models Prediction Renewable energy

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A Review of Feature Selection and Sentiment Analysis Technique in Issues of Propaganda

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第11期10卷 240-245页

作者： Ahmad, Siti Rohaidah Rodzi, Muhammad Zakwan Muhammad Shapiei, Nurlaila Syafira Yusop, Nurhafizah Moziyana Mohd Ismail, Suhaila Univ Pertahanan Nas Malaysia Fac Def Sci & Technol Dept Sci Comp Kuala Lumpur Malaysia

Propaganda is a form of communication that is used in influencing communities, or people in general, to push forward an agenda for a certain goal. Nowadays, there are different means used in distributing propaganda including postings on social media, illustrations, cartoons and animations, articles, TV and radio shows. This paper is focused on election propaganda. Candidates in elections would use propaganda as a form of communication to channel and deliver messages through social media. Sentiment analysis (SA) is then used in identifying the positive and negative elements within the propaganda itself, through analysing the related documents, social media, articles or forums. This paper presents the various techniques used by previous researchers in issues of propaganda using SA, which include feature selection to remove irrelevant features and sentiment methods to identify sentiment in documents or others. Feature selection is a dominant side in sentiment analysis due to content of textual has a high measurement classification that can jeopardize SA classification interpretation. This paper also explores several SA techniques to identify sentiments in issues of propaganda. This study has also attempted to identify the use of swarm algorithms as a suitable feature selection method in SA for propaganda issues.

关键词： Sentiment analysis feature selection swarm algorithm propaganda

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