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A green energy model for resource allocation in computational grid using differential evolution algorithm

WIRELESS NETWORKS 2021年第7期27卷 4773-4794页

作者： Rafie, Mahnaz Islamic Azad Univ Dept Comp Engn Ramhormoz Branch Ramhormoz Iran

Energy loss due to the complete equipping and overstatement of infrastructure networks has recently prompted internet service providers to reduce energy consumption. Increasing energy costs and environmental awareness have recently raised concerns about grid power consumption and the underlying structure of services;therefore, one of the top priorities for service providers is to reduce energy consumption. In this paper, a method based on the differential evolution optimization algorithm is introduced to reduce the energy consumption costs, network load balancing, and execution time. To increase the convergence speed in the optimization algorithm, two Mamdani fuzzy inference systems have been used which are used in the optimization objective function to determine the fitness amount of each allocation. To better understand proposed algorithm behavior, the results of the differential evolution algorithm are compared with the AGA and MPGA methods. Simulation results show that the proposed algorithm achieves more than 30% performance improvement in comparison with the previous algorithms.

关键词： differential evolution algorithm Green energy Grid Resource allocation

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Determination of induction motor parameters with differential evolution algorithm

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NEURAL COMPUTING & APPLICATIONS 2012年第8期21卷 1995-2004页

作者： Arslan, Mustafa Cunkas, Mehmet Sag, Tahir Selcuk Univ Fac Tech Educ Elect & Comp Educ TR-42003 Konya Turkey Selcuk Univ Dept Elect Tech Sci Vocat Sch TR-42003 Konya Turkey

In this study, the determination of equivalent circuit parameters of induction motors is carried out with differential evolution algorithm (DEA) and genetic algorithm (GA). As an objective function in the algorithms, the sum torque error at zero speed, pull-out, and rated speed is used. The determination of equivalent circuit parameters is performed with three induction motors of 2.2, 5.5, and 37 kW. In particular, the search ability of DEA is compared with GA by using the same population size, number of iteration, and crossover rate. In addition, the effects of the obtained equivalent circuit parameters on induction motors characteristics are investigated and presented with graphics. The results show that the use of DEA instead of GA increases the convergence sensitivity and reduces the simulation time.

关键词： Inductions motor Parameter determination differential evolution algorithm Genetic algorithm

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A multi-objective differential evolution algorithm for parallel batch processing machine scheduling considering electricity consumption cost

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COMPUTERS & OPERATIONS RESEARCH 2018年 96卷 55-68页

作者： Zhou, Shengchao Li, Xiaolin Du, Ni Pang, Yan Chen, Huaping Cent South Univ Forestry & Technol Sch Logist Changsha 410004 Hunan Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China Hunan Agr Univ Sch Engn Changsha 410128 Hunan Peoples R China Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China

The manufacturing industry consumes massive amounts of energy and produces great numbers of greenhouse gases every year. Recently, an increasing attention has been paid to the energy efficiency of the manufacturing industry. This paper considers a parallel batch processing machine (BPM) scheduling problem in the presence of dynamic job arrivals and a time-of-use pricing scheme. The objective is to simultaneously minimize makespan, a measure of production efficiency and minimize total electricity cost (TEC), an indicator for environmental sustainability. A BPM is capable of processing multiple jobs at a time, which has wide applications in many manufacturing industries such as electronics manufacturing facilities and steel-making plants. We formulate this problem as a mixed integer programming model. Considering the problem is strongly NP-hard, a multi-objective differential evolution algorithm is proposed for effectively solving the problem at large scale. The performance of the proposed algorithm is evaluated by comparing it to the well-known NSGA-II algorithm and another multi-objective optimization algorithm AMGA. Experimental results show that the proposed algorithm performs better than NSGA-II and AMGA in terms of solution quality and distribution. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable scheduling Total electricity cost Batch processing machines differential evolution algorithm Multi-objective optimization

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Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm

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MEMETIC COMPUTING 2024年第1期16卷 29-44页

作者： Han, Ning Chen, Yinnan Ye, Lingjuan Zhao, Xinchao Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China

Portfolio optimization will apply the concept of diversification across asset classes, which means investing in a wide variety of asset types and classes for a risk-mitigation strategy. Portfolio optimization is a way to maximize net gains in a portfolio while minimizing risk. A portfolio means investing in a wide variety of asset types and classes for a risk-mitigation strategy by the investor. In this paper, factor analysis and cluster algorithm are used to screen stocks and an improved differential evolution algorithm for solving portfolio optimization model is proposed. By comprehensively analyzing the stock data with factor analysis and k-means clustering algorithm, it has found that important factors have important effect on stock price movement, and finally 10 stocks are selected with investment value. Besides, a Mean-Conditional Value at Risk (CVaR) model is constructed, which takes into account both the cost function and the diversification constraint. Finally, a second-order memetic differential evolution (SOMDE) algorithm is presented for solving the proposed model. The experiments show that the proposed SOMDE algorithm is valid for solving the Mean-CVaR model and that factor analysis for stock selection can benefit portfolio with higher return and less risk greatly.

关键词： Portfolio optimization Factor analysis Mean-CVaR model differential evolution algorithm Memetic algorithm

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Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3923-3937页

作者： Civicioglu, Pinar Besdok, Erkan Gunen, Mehmet Akif Atasever, Umit Haluk Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Engn Fac Dept Geomat Engn Kayseri Turkey

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

关键词： Cuckoo search algorithm Artificial bee colony algorithm differential evolution algorithm Backtracking search optimization Particle swarm optimization

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Optimized differential evolution algorithm for Software Testing

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第1期12卷 215-226页

作者： Gou, Xiaodong Huang, Tingting Yang, Shunkun Su, Mengxuan Zeng, Fuping Beihang Univ Sch Reliabil & Syst Engn Beijing 100083 Peoples R China Univ Melbourne Melbourne Vic 3010 Australia

differential evolution (DE) algorithms for software testing usually exhibited limited performance and stability owing to possible premature-convergence-related aging during evolution processes. This paper proposes a new framework comprising an antiaging mechanism, that is, a rebirth strategy with partial memory against aging, for the existing DE algorithm and a specialized fitness function. The results of application of the proposed framework to instantiate three DE algorithms with different mutation schemas indicate that it significantly improved their effectiveness, performance, and stability. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Software testing Test data generation differential evolution algorithm Premature convergence Anti-aging Rebirth strategy

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Modelling the influence of pH, fluoride, and caffeine on the corrosion resistance of TiMo alloys by artificial neural networks developed with differential evolution algorithm

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MATERIALS AND CORROSION-WERKSTOFFE UND KORROSION 2015年第9期66卷 982-994页

作者： Mareci, D. Dragoi, E. -N. Bolat, G. Chelariu, R. Gordin, D. Curteanu, S. Gheorghe Asachi Tech Univ Iasi Fac Chem Engn & Environm Protect Iasi 700050 Romania Gheorghe Asachi Tech Univ Iasi Fac Mat Sci & Engn Iasi 700050 Romania INSA Rennes F-35708 Rennes France

Titanium (Ti) and its alloys are widely used in dental applications due to the excellent corrosion resistance and mechanical properties. However, it has been reported that Ti is sensitive to fluor ions (F-) and lactic acid. Corrosion behaviour of the TiMo alloys, together with the currently used metallic biomaterial commercial pure titanium (Cp-Ti), was investigated considering the use of alloys for dental applications. All the samples were examined using electrochemical impedance spectroscopy (EIS) in acidic artificial saliva with NaF and/or caffeine, at 37 degrees C. Equivalent circuits were used for modeling EIS data, in order to characterize samples surface and better understanding the effect of Mo addition on Cp-Ti. The TiMo alloys appear to possess superior corrosion resistance than Cp-Ti in all electrochemical media. In addition, a modelling technique based on differential evolution and artificial neural network was applied. The scope of this procedure was to determine an efficient model of the process and to eliminate the need for new experiments based on the predictions provided by the developed models.

关键词： acidic artificial saliva artificial neural networks caffeine differential evolution algorithm TiMo alloys

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A Non-Dominated Sorting Cooperative Co-evolutionary differential evolution algorithm for Multi-Objective Layout Optimization

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IEEE ACCESS 2017年 5卷 14468-14477页

作者： Zhang, Zi-Hui Zhong, Chong-Quan Xu, Zhi-Zheng Teng, Hong-Fei Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Different from traditional multi-objective evolutionary algorithms (MOEAS), multi-objective cooperative co-evolutionary algorithms (MOCCEAs) divide the decision variables into several subproblems to optimize. Solutions of each subproblem are evaluated by complete solutions formed through combining representative solutions from all subproblems. Therefore, the combination of representative solutions is a key issue in MOCCEAs. To improve the capability of MOCCEAs to complex multi objective optimization problems, we propose a non-dominated sorting cooperative co-evolutionary differential evolution algorithm (NSCCDE). The proposed NSCCDE uses an external archive for storing complete solutions to establish a new collaboration mechanism, which forms a complete solution by combining collaborators from each subpopulation as well as from the external archive. On the one hand, the external archive is updated continuously through non-dominated sorting of complete solutions, which is conducive to speeding up the convergence. On the other hand, the external archive evolves itself through spatial dispersal and mutation operation to increase the diversity. The performance of proposed NSCCDE is then evaluated on a suite of satellite module layout optimization problem. Experimental results demonstrate that the proposed algorithm outperforms NSCCGA and NSGA-II.

关键词： Multi-objective layout optimization cooperative co-evolution differential evolution algorithm satellite module

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An effective and efficient differential evolution algorithm for the integrated stochastic joint replenishment and delivery model

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KNOWLEDGE-BASED SYSTEMS 2012年 36卷 104-114页

作者： Wang, Lin Dun, Cai-Xia Bi, Wen-Jie Zeng, Yu-Rong Cent S Univ Sch Business Changsha 410083 Hunan Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Sch Informat Management Wuhan 430205 Hubei Peoples R China

As an important managerial problem, the practical joint replenishment and delivery (JRD) model under stochastic demand can be regarded as the combination of a joint replenishment problem and traveling salesman problem, either one is an NP-hard problem. However, due to the JRD's difficult mathematical properties, high quality solutions for the problem have eluded researchers. This paper firstly proposes an effective and efficient hybrid differential evolution algorithm (HDE) based on the differential evolution algorithm (DE) and genetic algorithm (GA) that can solve this NP-hard problem in a robust and precise way. After determining the appropriate parameters of the HDE by parameters tuning test, the effectiveness and efficiency of the HDE are verified by benchmark functions and numerical examples. We compare the HDE with the available best approach and find that the HDE can always obtain the slightly lower total costs under some situations. Compared with another popular evolutionary algorithm, results of numerical examples also show HDE is faster than GA and the convergence rate of HDE is higher than GA. HDE is a strong candidate for the JRD under stochastic demand. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Joint replenishment-delivery Stochastic demand Periodic review differential evolution algorithm Genetic algorithm

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Exergoeconomic analysis and optimization of a solar based multigeneration system using multiobjective differential evolution algorithm

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JOURNAL OF CLEANER PRODUCTION 2018年 170卷 978-990页

作者： Rashidi, Halimeh Khorshidi, Jamshid Univ Hormozgan Mech Engn Dept Bandar Abbas Iran

This paper presents and analyzes a multigeneration energy system that consists of a reverse osmosis desalination unit, water heater, organic Rankine cycle, photovoltaic solar collectors, and a single effect absorption chiller. In doing so, energy and exergy analysis are first performed to evaluate the performance of the system and determine the irreversibility of each component. Next, considering minimizing total cost rate and maximizing exergy efficiency as two objective functions, a multiobjective optimization approach based on differential evolution algorithm is proposed to determine the best design parameters. A self-adaptive technique is utilized to deal with the search capability, population diversity, and convergence speed of the proposed optimization algorithm. An external archive list is used to save all nondominated optimal solutions during the optimization. Dynamic crowding distance approach is employed to decrease archiving size without losing its characteristics. Furthermore, a fuzzy clustering approach is used to select the desired solution among the Pareto-optimal solutions. Simulation results are compared with two other multiobjective optimization algorithms and effectiveness of the proposed optimization method is verified using various indices. Finally, a sensitivity analysis is employed to evaluate effects of design parameters on exergy efficiency and total cost rate of the system. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multigeneration energy Photovoltaic solar system Exergy analysis Multiobjective optimization differential evolution algorithm Sensitivity analysis Pareto frontier

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