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A modified differential evolution algorithm for unconstrained optimization problems

NEUROCOMPUTING 2013年 120卷 469-481页

作者： Zou, Dexuan Wu, Jianhua Gao, Liqun Li, Steven Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ S Australia Div Business Adelaide SA 5001 Australia

A modified differential evolution algorithm (MDE) is proposed to solve unconstrained optimization problems in this paper. Gauss distribution and uniform distribution have one thing in common, that is randomness or indeterminateness. Due to this characteristic, MDE employs both distributions to adjust scale factor and crossover rate, which is useful to increase the diversity of the entire population. To guarantee the quality of the swarm, MDE uses an external archive, and some solutions of high quality in this external archive can be selected for candidate solutions. MDE adopts two common mutation strategies to produce new solutions, and the information of global best solution is more likely to be utilized for the mutation during late evolution process, which is beneficial to improving the convergence of the proposed algorithm. In addition, a central solution is generated in terms of all the other candidate solutions, and it can provide a potential searing direction. Experimental results show that MDE algorithm can yield better objective function values than the other six DE algorithms for some unconstrained optimization problems, thus it is an efficient alternative on solving unconstrained optimization problems. (c) 2013 Elsevier B.V. All rights reserved.

关键词： modified differential evolution algorithm Gauss distribution Uniform distribution External archive Mutation Central solution

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A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem

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ANNALS OF OPERATIONS RESEARCH 2024年第1期342卷 621-656页

作者： Pitakaso, Rapeepan Sethanan, Kanchana Tan, Kim Hua Kumar, Ajay Ubon Ratchathani Univ Fac Engn Dept Ind Engn Artificial Intelligence Optimizat SMART Lab Ratchathani Thailand Khon Kaen Univ Fac Engn Ind Engn Dept Res Unit Syst Modeling Ind Khon Kaen 40002 Thailand Univ Nottingham Business Sch Nottingham England EMLYON Business Sch Dept Operat Data & Artificial Intelligence Ecully France

This research focuses on the development of a novel artificial multiple intelligence system (AMIS), which is more flexible and effective than existing techniques for determining vegetable crop land allocation. Eight intelligence boxes (IBs) have been newly designed to serve as AMIS improvement tools presented in this study. Furthermore, a novel formula has been developed to efficiently select the appropriate IB for various types of problems. The developed method will be incorporated into a vegetable land allocation decision support system. The decision-making of the planning about land allocation for crops, including what to grow and what is in demand during specific periods, was performed while considering important factors such as production yield, crop planting and harvesting time, vegetable price fluctuations, and plant incompatibility, leading to a sustainable production system and achieving the highest prices and annual income. Moreover, the developed vegetable crop land allocation models yield the similarity of the average profit per area, so farmers could plan their crops accordingly. To solve the problem, a mathematical model was proposed to solve a small-sized problem, while a novel metaheuristic called the Artificial Multiple Intelligence System (AMIS) was applied to solve larger-sized problems. The computational results revealed that AMIS outperformed all other traditional methods used for comparison in this research. The solution of AMIS was higher in quality than traditional methods such as differential evolution (DE), Multi-Agent Simulated Quenching (MASQ), and Genetic algorithm (GA) by 21.78, 16.38, and 22.79%, respectively.

关键词： OR in agriculture Artificial multiple intelligence system Cooperatives crop planning AMIS embedded decision support system modified differential evolution algorithm Multi agent simulated quenching

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Runoff Forecasting of Machine Learning Model Based on Selective Ensemble

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WATER RESOURCES MANAGEMENT 2023年第11期37卷 4459-4473页

作者： Liu, Shuai Qin, Hui Liu, Guanjun Xu, Yang Zhu, Xin Qi, Xinliang Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Peoples R China China Yangtze Power Co Ltd Dept Water Resources Management Yichang 443133 Peoples R China

Reliable runoff forecasting plays an important role in water resource management. In this study, we propose a homogeneous selective ensemble forecasting framework based on modified differential evolution algorithm (MDE) to elucidate the complex nonlinear characteristics of hydrological time series. First, the same type of component learners was selected to form the average ensemble model, which was then trained using the training set to obtain preliminary prediction results. Subsequently, the MDE method was applied to improve the performance of the differential evolution algorithm with respect to low solution accuracy and premature convergence. MDE assigns weights according to the performance of each component learner in the ensemble model to obtain the selective ensemble model structure on the validation set. Finally, the selective ensemble framework was verified on the test set. Experiments were conducted on the runoff data of four important hydrological stations in the Yangtze River Basin. The results showed that the forecast framework can obtain better prediction accuracy and generalization performance than the average ensemble models composed of four classical learners, and can improve prediction accuracy for hydrological forecasting.

关键词： Runoff forecasting Hydrological time series modified differential evolution algorithm Selective ensemble forecasting

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A novel variable neighborhood strategy adaptive search for SALBP-2 problem with a limit on the number of machine's types

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ANNALS OF OPERATIONS RESEARCH 2023年第1-2期324卷 1501-1525页

作者： Pitakaso, Rapeepan Sethanan, Kanchana Jirasirilerd, Ganokgarn Golinska-Dawson, Paulina Ubon Ratchathani Univ Dept Ind Engn Metaheurist Logist Optimizat Lab Fac Engn Ubon Ratchathani Thailand Khon Kaen Univ Dept Ind Engn Res Unit Syst Modelling Ind Fac Engn Khon Kaen 40002 Thailand Poznan Univ Tech Fac Engn Management 2 Jacka Rychlewskiego Str PL-60965 Poznan Poland

This paper presents the novel method variable neighbourhood strategy adaptive search (VaNSAS) for solving the special case of assembly line balancing problems type 2 (SALBP-2S), which considers a limitation of a multi-skill worker. The objective is to minimize the cycle time while considering the limited number of types of machine in a particular workstation. VaNSAS is composed of two steps, as follows: (1) generating a set of tracks and (2) performing the track touring process (TTP). During TTP the tracks select and use a black box with neighborhood strategy in order to improve the solution obtained from step (1). Three modified neighborhood strategies are designed to be used as the black boxes: (1) modified differential evolution algorithm (MDE), (2) large neighborhood search (LNS) and (3) shortest processing time-swap (SPT-SWAP). The proposed method has been tested with two datasets which are (1) 128 standard test instances of SALBP-2 and (2) 21 random datasets of SALBP-2S. The computational result of the first dataset show that VaNSAS outperforms the best known method (iterative beam search (IBS)) and all other standard methods. VaNSAS can find 98.4% optimal solution out of all test instances while IBS can find 95.3% optimal solution. MDE, LNS and SPT-SWAP can find optimal solutions at 85.9%, 83.6% and 82.8% respectively. In the second group of test instances, we found that VaNSAS can find 100% of the minimum solution among all methods while MDE, LNS and SPT-SWAP can find 76.19%, 61.90% and 52.38% of the minimum solution.

关键词： Variable neighbourhood strategy adaptive search Assembly line balancing problem type 2 modified differential evolution algorithm Large neighbourhood search

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Edge boundary variable neighborhood strategy adaptive search for a vegetable crop land allocation problem

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2022年第3期17卷 166-174页

作者： Nitisiri, Krisanarach Sethanan, Kanchana Pitakaso, Rapeepan Gen, Mitsuo Khon Kaen Univ Fac Engn Dept Ind Engn Res Unit Syst Modelling Ind Khon Kaen 40002 Thailand Ubon Ratchathani Univ Fac Engn Dept Ind Engn Metaheurist Logist Optimizat Lab Ubon Ratchathani Thailand Tokyo Univ Sci Fac Sci & Engn Noda Chiba Japan

This paper focuses on optimizing cropland allocation for planting vegetables for farmer cooperatives. In this research, planning about land allocation for crops including what to grow and what is in demand during specific periods is performed while considering important factors such as production yield per area, crop planting and harvesting time, price fluctuations of vegetables and plant incompatibility, so that farmers can plan their crops accordingly, and achieve the optimal prices and annual income. To solve the problem, the Edge Boundary Variable Neighborhood Strategy Adaptive Search (EB-VaNSAS), which is the first extension of Variable Neighborhood Strategy Adaptive Search (VaNSAS), was applied to significantly improve the solution quality of the traditional Variable Neighborhood Strategy. Two local searches, called Track Transition Method (TTM) and Multiplier Factor (MF) were developed and used for the first time in this paper. The numerical results show that the EB-VaNSAS outperforms all other proposed methods since EB-VaNSAS could find new optimal solutions more often during the simulation, while the local search-based heuristics tend to be trapped in some local optima, and the DE lacks search intensification.

关键词： Variable neighborhood strategy adaptive search cooperatives crop planning modified differential evolution algorithm large neighborhood search

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The modified differential evolution algorithm (MDEA)

The Modified Differential Evolution Algorithm (MDEA)

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4th International Scientific Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Ramezani, Fatemeh Lotfi, Shahriar Coll Nabi Akram Dept Comp Engn Tabriz Iran Univ Tabriz Dept Comp Sci Tabriz Iran

ISBN: (纸本)9783642284939

differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms. DE has drawn the attention of many researchers resulting in a lot of variants of the classical algorithm with improved performance. This paper presents a new modified differential evolution algorithm for minimizing continuous space. New differential evolution operators for realizing the approach are described, and its performance is compared with several variants of differential evolution algorithms. The proposed algorithm is based on the idea of performing biased initial population. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed differential evolution algorithms. The results indicate that the proposed algorithm is able to arrive at high quality solutions in a relatively short time limit: for the largest publicly known problem instance, a new best solution could be found.

关键词： modified differential evolution algorithm differential evolution algorithm Optimization Biased Population

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Solving a special case of the generalized assignment problem using the modified differential evolution algorithms: A case study in sugarcane harvesting

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Journal of Open Innovation: Technology, Market, and Complexity 2019年第1期5卷 5页

作者： Srivarapongse, Tassin Pijitbanjong, Phajongjit Department of Economics Faculty of Business Administration Rajamangala University of Technology Thanyaburi Patumthani 10900 Thailand Faculty of Industrial Technology Songkhla Rajabhat University Songkhla Songkhla 90000 Thailand

We proposed and created a methodology to solve a real-world problem, which is a special case of the generalized assignment problem. The problem consists of assigning drivers to harvesters, which will then be assigned to harvest sugarcane in order to maximize daily profit. A set of drivers have various levels of experience. Therefore, a different capability to harvest sugarcane leads to a range of daily wages. Each harvester has different operating years and engine size, which affects its fuel consumption rate and capacity to harvest sugarcane, respectively. Assigning a worker to a harvester can improve the fuel consumption and efficiency of the harvester. We developed a mathematical model to reflect this problem and to solve it to find the maximum outcome using Lingo v.11 commercial optimization software. Since Lingo v.11 is limited to solving only small-size test instances, for medium to large test instances, four modified differential evolution (MDE) algorithms were used to solve the problem: MDE-1, MDE-2, MDE-3, and MDE-4. MDE-2 was found to be the best proposed heuristics because it has intensification and diversification ability. MDE has been tested with the case study. We tried to increase the daily profit by implementing three strategies: (1) change all harvesters that are more than five years old, (2) train drivers to reach maximum capacity, and (3) a combination of 1 and 2. Each strategy has a different investment. The breakeven point (number of days) to return the investment was calculated from the increase of daily profit. The computational results show that strategy 2 is the best because it has the quickest rate of investment return rate. However, this strategy has a disadvantage, since it is possible that drivers may leave the company if they have been highly trained. Moreover, strategy 1 has an acceptable break-even point at 392 days. © 2019 by the authors.

关键词： Assignment problem Generalized assignment problem modified differential evolution algorithm Sugarcane

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A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach

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ISA TRANSACTIONS 2018年 76卷 197-215页

作者： Li, Nailu Mu, Anle Yang, Xiyun Magar, Kaman T. Liu, Chao Yangzhou Univ Sch Hydraul Energy & Power Engn Yangzhou 225127 Jiangsu Peoples R China Xian Technol Univ Xian 200215 Shaanxi Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Wright State Res Inst AFRL RQVC Beavercreek OH 45431 USA

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Flap control system modified differential evolution algorithm Computation cost Varying range adaptation Composite adaptive internal model control

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Design of wide-area POD with resiliency using modified DEA for power systems with high penetration of renewable energy

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IET RENEWABLE POWER GENERATION 2019年第2期13卷 342-351页

作者： Setiadi, Herlambang Mithulananthan, Nadarajah Shah, Rakibuzzaman Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England CQUniv Sch Engn & Technol Perth WA 6000 Australia

Recently, the electromechanical oscillations are becoming the primary concerns in power grids due to the integration of renewable energy generations (REGs). The inherent characteristics of REGs, e.g. low or no inertia, stochastic generation, could lead to the lower damping margin in power systems. A supplemental control such as power system stabiliser or power oscillation damping (POD) controller is widely applied to augment the damping margin in power systems. However, the PSS or POD using local signal is not adequate to damp the oscillation contributed from different sources. Hence, a wide-area damping controller can be employed in this context. Nonetheless, the wide-area controller suffers from communication failure. Therefore, the wide-area controller with resilient to communication failures is inevitable. This study proposed a wide-area POD design method considering resiliency. A modified differential evolution algorithm is employed to synthesise the wide-area damping control. The two-area and Java-Indonesian (three-area) power systems are used to evaluate the performance of the proposed controller for oscillation damping. From the results, it is evident that the critical mode(s) of the system successfully damped by employing the proposed controller. It is also found that the proposed controller is robust against communication failures.

关键词： power system stability power grids evolutionary computation power system control damping power systems electromechanical oscillations power grids renewable energy generations lower damping margin supplemental control power system stabiliser wide-area damping controller wide-area controller suffers communication failure wide-area damping control three-area power oscillation damping controller REG wide-area POD design method modified differential evolution algorithm

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Adaptive differential evolution algorithm for efficient reactive power management

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APPLIED SOFT COMPUTING 2017年 53卷 336-351页

作者： Sakr, Walaa S. EL-Sehiemy, Ragab A. Azmy, Ahmed M. Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafrelsheikh Egypt Tanta Univ Fac Engn Elec Power & Machines Engn Dept Tanta Egypt

This paper introduces a proposed procedure to solve the optimal reactive power management (ORPM) problem based on a multi-objective function using a modified differential evolution algorithm (MDEA). The proposed MDEA is investigated in order to enhance the voltage profile as well as to reduce the active power losses by solving the ORPM problem. The ORPM objective function aims to minimize transmission power losses and voltage deviation considering the system constraints. The MDEA aims to enhance the convergence characteristic of the differential evolution algorithm through updating the self-adaptive scaling factor, which can exchange information dynamically every generation. The scaling factor dynamically adopts the global and local searches to efficiently eliminate trapping in local optima. In addition, a strategy is developed to update the penalty factor for alleviating the effects of various system constraints. Numerical applications of different case studies are carried out on three standard IEEE systems, i.e., 14 bus, 30-bus and 57-bus test systems. Also, the proposed procedure is applied on Western Delta Network, which is a real part of the Egyptian main grid system. The flexibility of synchronous machines to provide controllable reactive power is proven with less dependency on the discrete reactive power controllers, such as installing the switchable devices and variations of tap changers. The obtained results show the effectiveness of the proposed enhanced optimization algorithm as an advanced optimization technique that was successively implemented with good performance characteristics. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Adaptive penalty factor modified differential evolution algorithm Multi-objective function Optimal reactive power management

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