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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Naga Raju, M. Patra, Raj Kumar Nagesh, D. Kulkarni, Madhusudan Devi, R. Meena Cse GST Gitam Bengaluru India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Tontadarya College of Engineering Gadag Department of Ece India Agni College of Technology Cse Chennai India

ISBN: (纸本)9798350313413

Workflow scheduling is a well-explored topic for research in cloud computing, that focuses on maximizing the usage of cloud resources for different workflow activities. In this research, the topic of dynamic workflow scheduling is considered by addressing it as a Dynamic Multi-Objective optimization Problem (DMOP). The dynamism in this challenge originates from two key sources: resource failures and the possible fluctuation in the number of goals. Resource failures may arise from software or hardware problems, producing the first form of dynamism. Furthermore, real-world circumstances in cloud computing might lead to changes in the number of goals throughout the execution of a process, providing the second source of dynamism. To tackle this issue, this research introduces a novel hybrid optimization algorithm that employs both the Firefly algorithm and the cuckoo search algorithm. The Firefly algorithm, inspired by the flashing activity of fireflies, aids in fast exploring the search space and fine-tuning solutions, whereas Cuckoo search maximizes the exploitation of the finest solutions found. This hybrid approach takes advantage of Firefly's global search capabilities and Cuckoo's local search intensification, resulting in a robust and adaptive scheduling strategy that can effectively allocate computational tasks to cloud resources, optimizing memory utilization, cost, energy consumption and makespan in dynamic cloud environments. the proposed approach FFBSO has showed remarkable results with a lowering the make span of only 10s, illustrating its extraordinary efficiency in managing tasks. Furthermore, when submitted to 400 jobs using the proposed technique, the model obtained an excellent energy consumption of 11kwh. In addition to its memory usage ability, the suggested model also succeeded in memory usage, obtaining a particularly low value of 87%. © 2023 IEEE.

关键词： Cuckoo Search algorithm Firefly algorithm hybrid optimization algorithm Makespan Workflow Scheduling

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Developing an optimal layout design of a satellite system by considering natural frequency and attitude control constraints

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AEROSPACE SCIENCE AND TECHNOLOGY 2017年 71卷 172-188页

作者： Fakoor, Mahdi Zadeh, Parviz Mohammad Eskandari, Homa Momeni Univ Tehran Fac New Sci & Technol Tehran Iran

In recent years, there has been a growing research interest in layout design optimization of satellite systems. The layout design optimization of a satellite system is a complex process having a large number of design variables and constraints. This paper presents a hybrid optimization algorithm, which globally explores the design search space using Particle Swarm optimization (PSO) and gradient-based Sequential Quadratic Programming (SQP) to rapidly locate optimum design point. The majority of the previous research works mainly focused on finding reasonable placement of components in satellite layout design, with some specific requirements, which are essential for the satellite stability, control and performance such as attitude control, non-interference and overlap constraints. In this study, additional requirements such as structural stiffness and natural frequency constraints are also considered. The proposed approach is employed on a simplified international global communication satellite. The obtained results indicate that the consideration of natural frequency and attitude control constraints in the configuration layout design of a satellite system can significantly improve the stability and control of the satellite and thus frequency coupling between satellite and launcher can be prevented. In addition, the results indicate that the proposed method provides an effective way of solving layout design optimization problem of satellite systems. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Global optimization Layout design optimization hybrid optimization algorithm Sequential quadratic programming (Su) Particle swarm optimization (PSO)

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hybrid Differential Evolution algorithm Employed for the Optimum Design of a High-Speed PMSM Used for EV Propulsion

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2017年第12期64卷 9824-9833页

作者： Fodorean, Daniel Idoumghar, Lhassane Brevilliers, Mathieu Minciunescu, Paul Irimia, Cristi Tech Univ Cluj Napoca Dept Elect Machines & Drives Cluj Napoca 400114 Romania Univ Haute Alsace Math & Appl Informat Lab F-68100 Mulhouse France ICPE SA Bucharest 030138 Romania Siemens Ind Software Brasov 500203 Romania

This paper presents a new optimization approach, based on a hybrid algorithm, used for the design of a new high-speed permanent magnet synchronous machine (HS-PMSM). The HS-PMSM is used for the electromagnetic propulsion system (i.e., the machine will be connected to a magnetic gear) of an electric vehicle. The hybrid optimization algorithm combines two differential evolution methods and a memory mechanism, in order to increase the quality of the algorithm. The evolution of the rotor structure of the HS-PMSM (running at 22 000 r/min) is presented, as well as some aspects of the mechanical risks due to centrifugal forces. The best suited variant will be optimized based on the new optimization approach and the solution will be numerically and experimentally validated.

关键词： Electromagnetic propulsion system for electric vehicle (EV) high-speed permanent magnet synchronous machine (HS-PMSM) hybrid optimization algorithm

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A new modeling and solution method for optimal energy flow in electricity-gas integrated energy system

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2019年第9期43卷 4322-4343页

作者： Tan, Yuhua Wang, Xin Zheng, Yihui Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity-gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity-gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20-node gas system combined with IEEE 30-bus test system.

关键词： electric power system hybrid optimization algorithm integrated energy system natural gas system optimal energy flow optimal power flow optimal gas flow

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A hybrid equilibrium optimizer algorithm for multi-level image segmentation

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 4648-4678页

作者： Qi, Hong Zhang, Guanglei Jia, Heming Xing, Zhikai Northeast Forestry Univ Sch Informat & Comp Engn Harbin Heilongjiang Peoples R China Sanming Univ Sch Informat Engn Sanming Fujian Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China

Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

关键词： wood fiber image segmentation 3DOtsu equilibrium optimizer algorithm grasshopper optimization algorithm hybrid optimization algorithm

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Optimized Tilt Fractional Order Cooperative Controllers for Preserving Frequency Stability in Renewable Energy-Based Power Systems

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IEEE ACCESS 2021年 9卷 8261-8277页

作者： Elmelegi, Ahmed Mohamed, Emad A. Aly, Mokhtar Ahmed, Emad M. Mohamed, Al-Attar Ali Elbaksawi, Osama Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Univ Tecn Federico Santa Maria Elect Engn Dept Valparaiso 2390123 Chile Jouf Univ Coll Engn Dept Elect Engn Sakaka 2014 Saudi Arabia Aswan Univ Fac Engn AWCRC Aswan 81542 Egypt Port Said Univ Fac Engn Dept Elect Engn Port Said 42526 Egypt

The low system inertia and the high sensitivity to load and generation fluctuations represent the main challenges for future ambitious plans of modern power systems accompanied by high penetrations levels of the renewable energy sources (RESs). Therefore, this article presents a new approach for solving the load frequency control (LFC) in addition to the virtual inertia control (VIC) in interconnected RESs penetrated power systems using cooperative tilt-based controllers and a hybrid modified particle swarm optimization with genetic algorithm (MPSOGA). The VIC system is adopted using superconducting magnetic energy storage (SMES) to provide sufficient inertial energy for system stability. Two tilt-based controllers are employed in each area using the tilt-integral-derivative (TID) controller for the SMES and TID with filter (TIDF) for the LFC function. The cooperative optimum design of the TID/TIDF controllers leads to the enhancement of frequency stability in studied two-area power systems. The formulated optimization process aims to minimize the frequency nadir settling time during abrupt changes of RESs and/or load changes, considering the cooperative control of LFC and VIC. The proposed approach has been applied to a case study consisting of two-area power systems, connected via hybrid high voltage DC/AC (hybrid HVAC/HVDC) tie-line, integrated with distributed conventional generations, photovoltaic (PV), and wind generation systems. Performance analysis has been conducted to demonstrate the effectiveness of the proposed method is compared to the genetic algorithm (GA) and particle-swarm optimization (PSO) using high fluctuations of renewable generations under extreme changes in loading conditions and physical parameters variation. The obtained results show the superiority of MPSOGA approach on the other competitive optimization techniques.

关键词： Licenses hybrid optimization algorithm interconnected power systems load frequency control renewable energy sources (RESs) superconducting magnetic energy storage (SMES) virtual inertia control

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A solution to the ORPD problem and critical analysis of the results

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ELECTRICAL ENGINEERING 2018年第1期100卷 253-265页

作者： Radosavljevic, Jordan Jevtic, Miroljub Milovanovic, Milo Univ Pritina Kosovska Mitrovica Fac Tech Sci Kneza Milosa St 7 Kosovska Mitrovica 38220 Serbia

One of the most important conditions for economic and secure operation of electric power system is the optimal reactive power dispatch (ORPD). The ORPD is achieving by appropriate coordination of the equipments which manage the reactive power flows to minimize the real power loss and/or improve the voltage profile of the power system. Mathematically, the ORPD problem can be formulated as a nonlinear optimization problem with constraints. In this paper, a hybrid PSOGSA algorithm is proposed for solving the ORPD problem. The results obtained by hybrid PSOGSA on standard IEEE 30-bus and IEEE 118-bus test systems are compared with other meta-heuristic optimization methods. In addition, a critical review of the ORPD results published in the recent literature is presented also.

关键词： Optimal reactive power dispatch hybrid optimization algorithm Power loss minimization, voltage profile improvement

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An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2020年第10期35卷 1520-1569页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Sinha, Nikhil K. Indian Inst Technol ISM Dept Elect Engn Dhanbad 826004 Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Bihar India

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.

关键词： cuckoo search algorithm enhanced grey wolf optimization hybrid optimization algorithm inter-area oscillations power system stabilizer STATCOM

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Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation algorithm with Limited Factors

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WATER RESOURCES MANAGEMENT 2023年第3期37卷 1207-1222页

作者： Zhao, Long Xing, Liwen Wang, Yuhang Cui, Ningbo Zhou, Hanmi Shi, Yi Chen, Sudan Zhao, Xinbo Li, Zhe Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Sichuan Peoples R China Henan Univ Sci & Technol Coll Hort & Plant Protect Luoyang 471000 Henan Peoples R China

The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations;the role of T, n, RH, wind speed (U-2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.

关键词： Back-propagation neural network hybrid optimization algorithm Reference evapotranspiration K-fold Weather forecast

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Estimation of Geoacoustic Properties of Marine Sediment Using a hybrid Differential Evolution Inversion Method

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IEEE JOURNAL OF OCEANIC ENGINEERING 2010年第1期35卷 59-69页

作者： Jiang, Yong-Min Chapman, N. Ross Gerstoft, Peter Univ Victoria Sch Earth & Ocean Sci Victoria BC V8W 3V6 Canada Univ Calif San Diego Scripps Inst Oceanog Marine Phys Lab La Jolla CA 92093 USA

This paper reports the inversion of midfrequency (1500-4500 Hz) chirps from a short-range transmission experiment conducted on the New Jersey Continental Shelf during the 2006 Shallow Water Experiment (SW06). The source was held at different depths and the sound signals were recorded at a vertical line array to investigate the interactions with the sea bottom at various grazing angles. Strong reflections from the sediment layer were seen in the data for all of the sources. Due to the presence of complex microstructures in the thermocline of the oceanic sound-speed profile, fluctuations both in amplitude and arrival time of the direct path arrivals were observed. Time variation of the water-column environment was also evident during the source transmissions. To mitigate the effects of the ocean environment on the seabed property estimation, a multistage optimization inversion was employed. The sound speed and the experimental geometry were inverted first using only the travel times of the water-column arrivals. The bottom sound speed and the sediment layer thickness were then inverted by matching the travel times of the bottom and sub-bottom reflections. The average of the estimated values for the sediment sound speed is 1598 m/s, consistent with in situ measurements from other experiments in the vicinity.

关键词： Differential evolution (DE) downhill simplex (DHS) empirical orthogonal function (EOF) geoacoustic inversion hybrid optimization algorithm sediment property travel-time inversion

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