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Journal articles on the topic 'Pareto differential evolution'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

ABBASS, H. A., and R. SARKER. "THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM." International Journal on Artificial Intelligence Tools 11, no. 04 (2002): 531–52. http://dx.doi.org/10.1142/s0218213002001039.

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The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is compe

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SARKER, RUHUL, and HUSSEIN A. ABBASS. "DIFFERENTIAL EVOLUTION FOR SOLVING MULTIOBJECTIVE OPTIMIZATION PROBLEMS." Asia-Pacific Journal of Operational Research 21, no. 02 (2004): 225–40. http://dx.doi.org/10.1142/s0217595904000217.

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The use of evolutionary strategies (ESs) to solve problems with multiple objectives [known as vector optimization problems (VOPs)] has attracted much attention recently. Being population-based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for two standard test problems, ou

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3

Singh, Shailendra Pratap, and Anoj Kumar. "Pareto based differential evolution with homeostasis based mutation." Journal of Intelligent & Fuzzy Systems 32, no. 5 (2017): 3245–57. http://dx.doi.org/10.3233/jifs-169268.

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Gao, Yuelin, and Junmei Liu. "Multiobjective Differential Evolution Algorithm with Multiple Trial Vectors." Abstract and Applied Analysis 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/172041.

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This paper presents a multiobjective differential evolution algorithm with multiple trial vectors. For each individual in the population, three trial individuals are produced by the mutation operator. The offspring is produced by using the crossover operator on the three trial individuals. Good individuals are selected from the parent and the offspring and then are put in the intermediate population. Finally, the intermediate population is sorted according to the Pareto dominance relations and the crowding distance, and then the outstanding individuals are selected as the next evolutionary pop

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Qu, Dan, Hualin Xiao, Huafei Chen, and Hongyi Li. "An improved differential evolution algorithm for multi-modal multi-objective optimization." PeerJ Computer Science 10 (March 14, 2024): e1839. http://dx.doi.org/10.7717/peerj-cs.1839.

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Multi-modal multi-objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi-modal multi-objective differential evolution (MMODE) algorithm, called MMODE_AP, for the suit of CEC’2020 benchmark functions. First, two adaptive mutation strategies are adopted to balance exploration and exploitation and improve the diversity in the evolution process. Then, the affinit

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Goudos, S. K., and J. N. Sahalos. "Pareto Optimal Microwave Filter Design Using Multiobjective Differential Evolution." IEEE Transactions on Antennas and Propagation 58, no. 1 (2010): 132–44. http://dx.doi.org/10.1109/tap.2009.2032100.

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Peng, Shunshun, and Taolin Guo. "Multi-Objective Service Composition Using Enhanced Multi-Objective Differential Evolution Algorithm." Computational Intelligence and Neuroscience 2023 (March 4, 2023): 1–10. http://dx.doi.org/10.1155/2023/8184367.

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In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto-optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto-optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi-objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dua

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Adeyemo, J. A., and O. O. Olofintoye. "Evaluation of Combined Pareto Multiobjective Differential Evolution on Tuneable Problems." International Journal of Simulation Modelling 13, no. 3 (2014): 276–87. http://dx.doi.org/10.2507/ijsimm13(3)2.264.

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Goudos, Sotirios K., Katherine Siakavara, Elias Vafiadis, and John N. Sahalos. "PARETO OPTIMAL YAGI-UDA ANTENNA DESIGN USING MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION." Progress In Electromagnetics Research 105 (2010): 231–51. http://dx.doi.org/10.2528/pier10052302.

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Wisittipanich, Warisa, and Voratas Kachitvichyanukul. "Mutation strategies toward Pareto front for multi-objective differential evolution algorithm." International Journal of Operational Research 19, no. 3 (2014): 315. http://dx.doi.org/10.1504/ijor.2014.059507.

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11

Yang, Yong, and Rong Li. "Techno-Economic Optimization of an Off-Grid Solar/Wind/Battery Hybrid System with a Novel Multi-Objective Differential Evolution Algorithm." Energies 13, no. 7 (2020): 1585. http://dx.doi.org/10.3390/en13071585.

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Techno-economic optimization of a standalone solar/wind/battery hybrid system located in Xining, China, is the focus of this paper, and reliable and economic indicators are simultaneously employed to address the problem. To obtain a more precise Pareto set, a novel multi-objective differential evolution algorithm is proposed, where differential evolution with a parameter-adaptive mechanism is applied in the decomposition framework. The algorithm effectiveness is verified by performance comparisons on the benchmark test problems with two reference algorithms: a non-dominated sorting genetic alg

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He, Xiaoguang, Cai Dai, and Zehua Chen. "Many-Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/259473.

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Pareto dominance is an important concept and is usually used in multiobjective evolutionary algorithms (MOEAs) to determine the nondominated solutions. However, for many-objective problems, using Pareto dominance to rank the solutions even in the early generation, most obtained solutions are often the nondominated solutions, which results in a little selection pressure of MOEAs toward the optimal solutions. In this paper, a new ranking method is proposed for many-objective optimization problems to verify a relatively smaller number of representative nondominated solutions with a uniform and wi

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Yu-long, Xu, and Zhao Ling-dong. "A Fast Two-objective Differential Evolutionary Algorithm based on Pareto-optimal Set." International Journal of Software Science and Computational Intelligence 8, no. 1 (2016): 46–59. http://dx.doi.org/10.4018/ijssci.2016010104.

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The two-objective differential evolution with Pareto-optimal set, which is researched in this paper. Firstly, it is found that there are some redundant computations in the classic multi-objective evolutionary algorithm, such as the NSGA-II. Then, based on the concept of Pareto-optimal set, the non-dominated solution sorted and its potential features, the authors propose a ranking method for solution that only handles the highest rank individuals in current population. The highlight of the proposed method is that during the ranking process, the individuals can be chosen into the next generation

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Rajesh, Kummari, and N. Visali. "Hybrid method for achieving Pareto front on economic emission dispatch." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3358. http://dx.doi.org/10.11591/ijece.v10i4.pp3358-3366.

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In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set

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K., Rajesh, and Visali N. "Hybrid method for achieving Pareto front on economic emission dispatch." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3358–66. https://doi.org/10.11591/ijece.v10i4.pp3358-3366.

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In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set

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Duan, Lini, Yuanyuan Liu, Haiyan Li, Kyung-Hye Park, and Kerang Cao. "Nondominated Sorting Differential Evolution Algorithm to Solve the Biobjective Multi-AGV Routing Problem in Hazardous Chemicals Warehouse." Mathematical Problems in Engineering 2022 (September 16, 2022): 1–20. http://dx.doi.org/10.1155/2022/3785039.

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For the multiple automated guided vehicle (multi-AGV) routing problems in the warehousing link of logistics, where the optimization objective is to minimize both the number of AGVs used and the maximum pickup time simultaneously, a nondominant sorting differential evolution (NSDE) algorithm is proposed. In the encoding and decoding stages, the pickup point area is divided. AGVs are allocated to each region according to the proposed rule based on avoiding duplicate paths. Meanwhile, the pickup points within the region can be adjusted to optimize the pickup paths and improve the pickup efficienc

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Chen, Bili, Wenhua Zeng, Yangbin Lin, and Qi Zhong. "An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems." Journal of Applied Mathematics 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/931630.

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An enhanced differential evolution based algorithm, named multi-objective differential evolution with simulated annealing algorithm (MODESA), is presented for solving multiobjective optimization problems (MOPs). The proposed algorithm utilizes the advantage of simulated annealing for guiding the algorithm to explore more regions of the search space for a better convergence to the true Pareto-optimal front. In the proposed simulated annealing approach, a new acceptance probability computation function based on domination is proposed and some potential solutions are assigned a life cycle to have

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18

Chaves-González, José M., and Miguel A. Pérez-Toledano. "Differential evolution with Pareto tournament for the multi-objective next release problem." Applied Mathematics and Computation 252 (February 2015): 1–13. http://dx.doi.org/10.1016/j.amc.2014.11.093.

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19

Pantoja-García, Jesús S., Miguel G. Villarreal-Cervantes, Consuelo V. García-Mendoza, and Víctor M. Silva-García. "Synergistic Design of the Bipedal Lower-Limb through Multiobjective Differential Evolution Algorithm." Mathematical Problems in Engineering 2019 (May 2, 2019): 1–17. http://dx.doi.org/10.1155/2019/2301714.

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The complexity in the design of bipedal robots has motivated the use of simple mechanisms to accomplish the desired locomotion task with a minimum control effort. Nevertheless, a diverse set of conflictive design criteria must be met to develop the bipedal gait. In this paper, the synergy in the eight-bar mechanism design criteria to satisfy the bipedal lower-limb behavior is promoted by proposing a Pareto-based Nonlinear Mixed Discrete-Continuous Constrained Multiobjective Optimization Problem and by improving the search in the optimizer through the inclusion of the Multiselection Strategy in

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Bohvalovs, Girts, Ruta Vanaga, Vita Brakovska, Ritvars Freimanis, and Andra Blumberga. "Energy Community Measures Evaluation via Differential Evolution Optimization." Environmental and Climate Technologies 26, no. 1 (2022): 606–15. http://dx.doi.org/10.2478/rtuect-2022-0046.

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Abstract Energy communities are paving the way for new cooperation opportunities related to energy consumption and energy production. Individuals unite in energy communities to reduce the costs related to energy consumption. Although previous work has mainly focused on energy exchange inside the community. This work aims to investigate the Pareto-optimal solutions to the transformation of a historical district into an energy community. For energy efficiency and production measure calculation, a system dynamics model is developed. Multiobjective differential evolution optimization method is emp

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Ying, Weiqin, Bin Wu, Yu Wu, Yali Deng, Hainan Huang, and Zhenyu Wang. "Efficient Conical Area Differential Evolution with Biased Decomposition and Dual Populations for Constrained Optimization." Complexity 2019 (February 20, 2019): 1–18. http://dx.doi.org/10.1155/2019/7125037.

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The constraint-handling methods using multiobjective techniques in evolutionary algorithms have drawn increasing attention from researchers. This paper proposes an efficient conical area differential evolution (CADE) algorithm, which employs biased decomposition and dual populations for constrained optimization by borrowing the idea of cone decomposition for multiobjective optimization. In this approach, a conical subpopulation and a feasible subpopulation are designed to search for the global feasible optimum, along the Pareto front and the feasible segment, respectively, in a cooperative way

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22

Ellie, Kai Steven, and Wenqiang Zhang. "Hybrid Enhanced Evolutionary Algorithm with Differential Evolution for Distributed No-Wait Flow-Shop Scheduling Problem." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4696–711. http://dx.doi.org/10.22214/ijraset.2023.54482.

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Abstract: The distributed no-wait flow-shop scheduling problem (DNWFSP) holds significant importance in real-world production environments. However, existing algorithms for solving the DNWFSP have limitations such as unsatisfactory solutions. Therefore, this paper proposes a hybrid enhanced multi-objective evolutionary algorithm (HEMOEA) with differential evolution (HEMOEA-DE) to solve DNWFSP with the criteria of minimizing makespan and total processing time. The algorithm incorporates a mathematical model for the DNWFSP. Effective encoding and decoding methods are employed, and DE is utilized

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23

Ghoneim, Sherif S. M., Mohamed F. Kotb, Hany M. Hasanien, Mosleh M. Alharthi, and Attia A. El-Fergany. "Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis." Sustainability 13, no. 14 (2021): 8113. http://dx.doi.org/10.3390/su13148113.

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A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorpora

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PANIGRAHI, C. K., R. CHAKRABARTI, and P. K. CHATTOPADHYAY. "ECONOMIC ENVIRONMENTAL DISPATCH BY A MODE TECHNIQUE." Journal of Circuits, Systems and Computers 17, no. 03 (2008): 499–512. http://dx.doi.org/10.1142/s0218126608004411.

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Multi-objective differential evolution (MODE) is proposed to handle economic environmental dispatch problem, which is a multi-objective optimization problem (MOOPs) with competing and noncommensurable objectives. The proposed approach has a good performance in finding a diverse set of solutions and in converging near the true Pareto-optimal set. Numerical results for two sample test systems have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of economic environmental dispatch problem in one single run. Simulation re

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Zhang, Yi, Hu Zhang, and Chao Lu. "Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm." Mathematical Problems in Engineering 2012 (2012): 1–23. http://dx.doi.org/10.1155/2012/734193.

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In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell) by introducing differential evolution strategy into

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Jingfeng, Yan, Li Meilian, Xu Zhijie та Xu Jin. "A Simple Pareto Adaptive ε-Domination Differential Evolution Algorithm for Multi-Objective Optimization". Open Automation and Control Systems Journal 7, № 1 (2015): 338–45. http://dx.doi.org/10.2174/1874444301507010338.

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Gong, Wenyin, and Zhihua Cai. "An improved multiobjective differential evolution based on Pareto-adaptive -dominance and orthogonal design." European Journal of Operational Research 198, no. 2 (2009): 576–601. http://dx.doi.org/10.1016/j.ejor.2008.09.022.

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Mahate, Ram Kishan, and Himmat Singh. "MULTI-OBJECTIVE OPTIMAL REACTIVE POWER DISPATCH USING DIFFERENTIAL EVOLUTION." International Journal of Engineering Technologies and Management Research 6, no. 2 (2020): 27–38. http://dx.doi.org/10.29121/ijetmr.v6.i2.2019.353.

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Reactive power optimization is a major concern in the operation and control of power systems. In this paper a new multi-objective differential evolution method is employed to optimize the reactive power dispatch problem. It is the mixed–integer non linear optimization problem with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. The optimal VAR dispatch problem is developed as a nonlinear constrained multi objective optimization problem where the real power loss and fuel cost are to be minimized at the same

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Ram, Kishan Mahate, and Singh Himmat. "MULTI-OBJECTIVE OPTIMAL REACTIVE POWER DISPATCH USING DIFFERENTIAL EVOLUTION." International Journal of Engineering Technologies and Management Research 6, no. 2 (2019): 27–38. https://doi.org/10.5281/zenodo.2585477.

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Reactive power optimization is a major concern in the operation and control of power systems. In this paper a new multi-objective differential evolution method is employed to optimize the reactive power dispatch problem. It is the mixed–integer non linear optimization problem with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. The optimal VAR dispatch problem is developed as a nonlinear constrained multi objective optimization problem where the real power loss and fuel cost are to be minimized at the

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Ketabi, A., S. M. Nosratabadi, and M. R. Sheibani. "Optimal PMU Placement with Uncertainty Using Pareto Method." Mathematical Problems in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/501893.

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This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs) in state estimation considering uncertainty. State estimation has first been turned into an optimization exercise in which the objective function is selected to be the number of unobservable buses which is determined based on Singular Value Decomposition (SVD). For the normal condition, Differential Evolution (DE) algorithm is used to find the optimal placement of PMUs. By considering uncertainty, a multiobjective optimization exercise is hence formulated. To achieve this, DE algorithm based on Pareto optimum

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Sedak, Miloš, and Božidar Rosić. "Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm." Applied Sciences 11, no. 3 (2021): 1107. http://dx.doi.org/10.3390/app11031107.

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This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm

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Mehrvar, Ali, Ali Basti, and Ali Jamali. "Optimization of electrochemical machining process parameters: Combining response surface methodology and differential evolution algorithm." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 6 (2016): 1114–26. http://dx.doi.org/10.1177/0954408916656387.

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Electrochemical machining is a unique prevalent nonconventional manufacturing process used in different industries involving various process parameters, which greatly influence machining performance. Therefore, selection of proper and optimal parameters setting is a challenging issue. In this paper, differential evolution algorithm is applied to look for the optimum solution to this problem. Four parameters, i.e. voltage, tool feed rate, electrolyte flow rate, and electrolyte concentration; and two machining criteria, i.e. material removal rate and surface roughness (Ra) are considered as inpu

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Dong, Tao. "Dynamic economic emission dispatch of combined heat and power system based on multi-objective differential evolution algorithm." PLOS One 20, no. 6 (2025): e0326104. https://doi.org/10.1371/journal.pone.0326104.

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Engineering frequently deals with multi-objective optimization problems. In the scheduling of combined heat and power systems, the competing goals of economic cost and pollutant emission are challenging for conventional single-objective algorithms to handle, necessitating the use of effective multi-objective optimization algorithms. The research design improves the multi-objective differential evolution algorithm, which is constructed by making the scaling factor and crossover probability change adaptively, adopting non-dominated sorting, combining the congestion distance calculation to deal w

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Qasem, Sultan Noman, and Siti Mariyam Shamsuddin. "Memetic Elitist Pareto Differential Evolution algorithm based Radial Basis Function Networks for classification problems." Applied Soft Computing 11, no. 8 (2011): 5565–81. http://dx.doi.org/10.1016/j.asoc.2011.05.002.

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Ikudayisi, Akinola, Josiah Adeyemo, John Odiyo, Abimbola Enitan, and Roberto Revelli. "Optimum irrigation water allocation and crop distribution using combined Pareto multi-objective differential evolution." Cogent Engineering 5, no. 1 (2018): 1535749. http://dx.doi.org/10.1080/23311916.2018.1535749.

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Zhang, Qing Xin, Zhi Ping Fan, Shan Wei Zhang, and Hui Yang Yu. "The Mill Pacing Research Based on the Optimized Plate Model." Applied Mechanics and Materials 236-237 (November 2012): 213–18. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.213.

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In This paper, in the background of a plate milling system in China, various influences on mill pacing in plate rolling are taken into consideration, a plate model by using the objective optimization theory based on the field statistics are introduced. This model based on the differential evolution, that establishes the objective function of rolling quality-temperature waiting time-rolling time, and uses concurrent optimization for the plate rolling of various needs. The results could get a series of Pareto sets to content the different requirements of determining the contiguous slab’s out-sto

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Tarajo BUBA, Ahmed, and Lai Soon LEE. "A Differential Evolution for Optimization of Multiobjective Urban Transit Routing Problem." International Journal of Engineering & Technology 7, no. 3.20 (2018): 140. http://dx.doi.org/10.14419/ijet.v7i3.20.18999.

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In this paper, the urban transit routing problem is addressed by using a real-world urban transit network. Given the road network infrastructure and the demand, the problem consists in designing routes such that the service level as well as the operator cost are optimized. The optimality of the service level is measured in terms of average journey time and the route set length. A differential evolution approach is proposed to solve the problem. An improved sub-route reversal repair mechanism is introduced to deal with the infeasibility of route sets. Computational results on a real network pro

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Lotfi, Nasser, and Mazyar Ghadiri Nejad. "A New Hybrid Algorithm Based on Improved MODE and PF Neighborhood Search for Scheduling Task Graphs in Heterogeneous Distributed Systems." Applied Sciences 13, no. 14 (2023): 8537. http://dx.doi.org/10.3390/app13148537.

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Multi-objective task graph scheduling is a well-known NP-hard problem that plays a significant role in heterogeneous distributed systems. The solution to the problem is expected to optimize all scheduling objectives. Pretty large state-of-the-art algorithms exist in the literature that mostly apply different metaheuristics for solving the problem. This study proposes a new hybrid algorithm comprising an improved multi-objective differential evolution algorithm (DE) and Pareto-front neighborhood search to solve the problem. The novelty of the proposed hybrid method is achieved by improving DE a

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Roselyn, J. Preetha, D. Devaraj, and Subhransu Sekhar Dash. "Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems." International Journal of Emerging Electric Power Systems 14, no. 6 (2013): 591–607. http://dx.doi.org/10.1515/ijeeps-2013-0086.

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Abstract Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the applicatio

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Wang, Gao Ping, Meng Zhang, and Wei Wei Zhao. "A Novel Multiobjective Memetic Algorithm Based on IWO-DE and its Application in Nutrition Decision Making Problem." Advanced Materials Research 989-994 (July 2014): 1849–52. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1849.

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In this paper ,we discuss multiobjective optimization problems solved by Memetic algorithms. We present A novel multiobjective memetic algorithm based on invasive weed optimization and differential evolution (IWO-DE) to solve this class of problems .We present the Nutrition Prescription Model for Meals.the IWO-DE is applied to solve the nutrition decision making problem to map the Pareto-optimum front. The results in the problem show its effectiveness.

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Kaka, Jhansi Rani, and K. Satya Prasad. "Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification." Security and Communication Networks 2022 (January 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/7275433.

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Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image norma

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Singh, Avjeet, Lekhraj Lekhraj, Alok Kumar, and Anoj Kumar. "Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems." Trends in Sciences 18, no. 20 (2021): 17. http://dx.doi.org/10.48048/tis.2021.17.

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Simultaneous optimization of two or more objectives is an instance of multi-objective optimization (MOO). However, for most of the Multi-Objective Problems (MOPs), no single solution can optimize all the objective functions simultaneously. Evolutionary algorithms are a type of optimization algorithm used for constructing a well-distributed optimal front more quickly than a much more efficient approach. One of the most commonly used algorithms in this regard is differential evolution (DE). To solve the problem of non-dominated sorting for Multi-Objective DE (MODE), an approach is proposed that

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Chen, Zhi-Kun, Feng-Gang Yan, Xiao-Lin Qiao, and Yi-Nan Zhao. "Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution." International Journal of Antennas and Propagation 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1747843.

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A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the

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Zhang, Wenqiang, Chen Li, Mitsuo Gen, Weidong Yang, Zhongwei Zhang, and Guohui Zhang. "Multiobjective particle swarm optimization with direction search and differential evolution for distributed flow-shop scheduling problem." Mathematical Biosciences and Engineering 19, no. 9 (2022): 8833–65. http://dx.doi.org/10.3934/mbe.2022410.

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<abstract><p>As a classic problem of distributed scheduling, the distributed flow-shop scheduling problem (DFSP) involves both the job allocation and the operation sequence inside the factory, and it has been proved to be an NP-hard problem. Many intelligent algorithms have been proposed to solve the DFSP. However, the efficiency and quality of the solution cannot meet the production requirements. Therefore, this paper proposes a bi-objective particle swarm optimization with direction search and differential evolution to solve DFSP with the criteria of minimizing makespan and total

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Jariyatantiwait, Chatkaew, and Gary G. Yen. "5 by 5 Microstrip Antenna Array Design by Multiobjective Differential Evolution based on Fuzzy Performance Feedback." International Journal of Swarm Intelligence Research 7, no. 4 (2016): 1–22. http://dx.doi.org/10.4018/ijsir.2016100101.

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Differential evolution is often regarded as one of the most efficient evolutionary algorithms to tackle multiobjective optimization problems. The key to success of any multiobjective evolutionary algorithms (MOEAs) is maintaining a delicate balance between exploration and exploitation throughout the evolution process. In this paper, the authors develop an Improved version of the Fuzzy-based Multiobjective Differential Evolution (IFMDE) that exploits performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution progress. They apply the fuzzy i

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Ramakrishna, Rao Mamidi*1 &. Jagdish Mamidi2. "MULTI-OBJECTIVE DIFFERENTIAL EVOLUTION BASED ELECTROMAGNETIC DESIGN OF WIND-TURBINE GENERATOR." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 6, no. 7 (2019): 27–38. https://doi.org/10.5281/zenodo.3342894.

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Renewable energy particularly solar and wind are now being considered as mainstream energy source and are competing at par with conventional ones. Technological innovations and cost efficiencies are driving their growth across the globe and fast increasing their penetration into power grids. In wind generators, doubly fed induction generators (DFIG) have gained popularity due to their efficiency, speed variation, need for less control circuit power and four quadrant operations. However, power grid requirements are becoming stringent in performance needs requiring DFIGs to comply with the techn

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Yue, Liya, Pei Hu, Shu-Chuan Chu, and Jeng-Shyang Pan. "English Speech Emotion Classification Based on Multi-Objective Differential Evolution." Applied Sciences 13, no. 22 (2023): 12262. http://dx.doi.org/10.3390/app132212262.

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Speech signals involve speakers’ emotional states and language information, which is very important for human–computer interaction that recognizes speakers’ emotions. Feature selection is a common method for improving recognition accuracy. In this paper, we propose a multi-objective optimization method based on differential evolution (MODE-NSF) that maximizes recognition accuracy and minimizes the number of selected features (NSF). First, the Mel-frequency cepstral coefficient (MFCC) features and pitch features are extracted from speech signals. Then, the proposed algorithm implements feature

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DONG, NING, and YUPING WANG. "AN UNBIASED BI-OBJECTIVE OPTIMIZATION MODEL AND ALGORITHM FOR CONSTRAINED OPTIMIZATION." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (2014): 1459008. http://dx.doi.org/10.1142/s0218001414590083.

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Transforming a constrained optimization problem (COP) into a bi-objective optimization problem (BOP) is an efficient way to solve the COP. However, how to obtain a good balance between the objective function and the constraint violation function is not easy in BOP. To handle this issue, a novel unbiased bi-objective optimization model is proposed, in which both objective functions are equally treated. Furthermore, the novel model is shown to have the unique Pareto optimal vector under proper condition, and the Pareto optimal vector is exactly corresponding to the optimal solution of COP. Moreo

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Liu, Yi, Jun Guo, Huaiwei Sun, Wei Zhang, Yueran Wang, and Jianzhong Zhou. "Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/8215308.

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Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS) is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of

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Saravanan, R., S. Ramabalan, and C. Balamurugan. "Multiobjective trajectory planner for industrial robots with payload constraints." Robotica 26, no. 6 (2008): 753–65. http://dx.doi.org/10.1017/s0263574708004359.

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SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select

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