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An Improved Differential Evolution Algorithm for Solving High Dimensional Optimization Problem

Chunfeng Song, Yuanbin Hou
2015 International Journal of Hybrid Information Technology  
, an improved differential evolution algorithm with multi-population and multi-strategy(MPMSIDE) is proposed to solve high dimensional optimization problem.  ...  In order to improve the weak situation of the global search ability, the stability and time consuming of optimization of differential evolution(DE) algorithm in solving high dimensional optimization problem  ...  ACKNOWLEDGEMENTS This research was supported by the Youth Science Foundation (51405381) and the Special Foundation for Scientists of Xi'an university of science and technology (201314). References  ... 
doi:10.14257/ijhit.2015.8.10.16 fatcat:ntnugoyok5arvpdvzxnjxyvgdy

Modified Constrained Differential Evolution for Solving Nonlinear Global Optimization Problems [chapter]

Md. Abul Kalam Azad, M. G. P. Fernandes
2013 Studies in Computational Intelligence  
In this paper, we propose a modified constrained differential evolution based on different constraints handling techniques, namely, feasibility and dominance rules, stochastic ranking and global competitive  ...  Differential evolution has shown to be very efficient when solving global optimization problems with simple bounds.  ...  number η. mCDE algorithm: The algorithm of the herein proposed modified constrained differential evolution for constrained nonlinear optimization problems is described in Algorithm 1.  ... 
doi:10.1007/978-3-642-35638-4_7 fatcat:yjshmuvk5nefhjfat3tx7xiwxy

Front Matter

2020 2020 IEEE Congress on Evolutionary Computation (CEC)  
and Technology, Russian Federation P506 Enhancing Evolutionary Algorithms by Efficient Population Initialization for Constrained Problems [#24222] Saber Elsayed, Ruhul Sarker, Noha Hamza, Carlos Coello  ...  Evolution: Past, Present and Future Tuesday, July 21, 2:30PM-4:30PM, Room: CEC Room 3, Chair: Kai Qin, Swagatam Das 2:30PM Adaptive Guidance-based Differential Evolution with Iterative Feedback Archive  ... 
doi:10.1109/cec48606.2020.9185689 fatcat:zhe2kwbpjfa5nlbjqj5rsytnq4

An Adaptive Population Size Differential Evolution with Novel Mutation Strategy for Constrained Optimization [article]

Yuan Fu, Hu Wang, Meng-Zhu Yang
2018 arXiv   pre-print
Differential evolution (DE) has competitive performance on constrained optimization problems (COPs), which targets at searching for global optimal solution without violating the constraints.  ...  The proposed differential evolution algorithm, APDE-NS, is evaluated on the benchmark problems from CEC2017 constrained real parameter optimization.  ...  . 4) A unified differential evolution algorithm for constrained optimization problems (UDE) [36] . 5) Self-adaptive differential evolution algorithm for constrained real-parameter optimization (SaDE  ... 
arXiv:1805.04217v1 fatcat:wr7fbgbetze6zpt4y2h6vn3b6q

Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

Chun An Liu, Huamin Jia
2018 International Journal of Swarm Intelligence and Evolutionary Computation  
In this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is proposed.  ...  First, we review some existing excellent algorithms for solving NOCP; then, the nonlinear constrained optimization problem is transformed into a bi objective optimization problem.  ...  Acknowledgment This work is partially supported by The Planning Fund for the Humanities and Social Sciences of the Ministry of Education (No. 18YJA790053).  ... 
doi:10.4172/2090-4908.1000172 fatcat:jd6kzjsz7vh4jjvrrd5v4b6c7u

An Improved Backtracking Search Algorithm for Constrained Optimization Problems [chapter]

Wenting Zhao, Lijin Wang, Yilong Yin, Bingqing Wang, Yi Wei, Yushan Yin
2014 Lecture Notes in Computer Science  
The results show the improved backtracking search algorithm is effective and competitive for constrained optimization problems.  ...  This paper proposes an improved backtracking search algorithm for constrained optimization problems.  ...  algorithms for constrained optimization problems.  ... 
doi:10.1007/978-3-319-12096-6_20 fatcat:iqfh3gryjne6riawljxx4s7zle

Evolving Differentiable Gene Regulatory Networks [article]

Dennis G Wilson, Kyle Harrington, Sylvain Cussat-Blanc, Hervé Luga
2018 arXiv   pre-print
We detail an GPU-based implementation of differentiable GRNs, allowing for local optimization of GRN architectures with stochastic gradient descent (SGD).  ...  Using a standard machine learning dataset, we evaluate the ways in which evolution and SGD can be combined to further GRN optimization.  ...  The protein tags are constrained during optimization to be in [0.0, 1.0], and β and δ are constrained between the parameter values [β min , β max ] and [δ min , δ max ], which, for this run, were both  ... 
arXiv:1807.05948v1 fatcat:wudsfwd2sbahhf7asgw5xseizm

Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization [article]

Wei Huang, Tao Xu, Jun He, Kangshun Li
2018 arXiv   pre-print
This paper proposes a new multiobjective optimization differential evolution algorithm for constrained optimization.  ...  Then multiobjective optimization differential evolution using this projection operator is designed for constrained optimization.  ...  has significantly improved the solution quality when compared with CMODE, an state-of-the art MOEA for COPs, but is also very competitive with the EAs in CEC 2006 Competition and is ranked third.  ... 
arXiv:1805.00272v1 fatcat:66aipmd4nvfefly75c6ev6xkqq

Integration of Genetic Algorithm and Cultural Particle Swarm Algorithms for Constrained Optimization of Industrial Organization and Diffusion Efficiency Analysis in Equipment Manufacturing Industry

Xu Sheng CHEN, Ya Jie WANG, Hong Qi WANG
2013 Sensors & Transducers  
Firstly, improved topology structure of Particle swarm optimization algorithm. Secondly, using crossover strategy and niche competition mechanism.  ...  the manufacturing level of industry product differentiation and so on.  ...  Comparison of the Results before and after the Improvement For example, in reference [10] , the single objective nonlinear constrained optimization problem.  ... 
doaj:30f1070a45034f5997f2896cb7ec2019 fatcat:rvuw3h3sabg7rgd7vqs6sfdueq

Hybridization of Interval CP and Evolutionary Algorithms for Optimizing Difficult Problems [chapter]

Charlie Vanaret, Jean-Baptiste Gotteland, Nicolas Durand, Jean-Marc Alliot
2015 Lecture Notes in Computer Science  
The only rigorous approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains  ...  that cannot contain an optimal solution.  ...  We proposed a cooperative hybrid solver Charibde, in which a deterministic interval branch and contract cooperates with a stochastic differential evolution algorithm.  ... 
doi:10.1007/978-3-319-23219-5_32 fatcat:c7tisd56ezbxrbecjrhzum6ds4

Differential evolution based global best algorithm: an efficient optimizer for solving constrained and unconstrained optimization problems

Mert Sinan Turgut, Oguz Emrah Turgut
2020 SN Applied Sciences  
This study proposes an optimization method called Global Best Algorithm for successful solution of constrained and unconstrained optimization problems.  ...  This propounded method uses the manipulation equations of Differential Evolution, dexterously combines them with some of the perturbation schemes of Differential Search algorithm, and takes advantages  ...  [14] introduced an enhanced Differential Evolution (EDDE) algorithm by slightly changing the mutation scheme of the algorithm.  ... 
doi:10.1007/s42452-020-2426-8 fatcat:huidcl725jhova6r75aompr7by

Adaptive Range Composite Differential Evolution for Fast Optimal Reactive Power Dispatch

Ming Niu, Ning Zhou Xu, He Nan Dong, Yang Yang Ge, Yi Tao Liu, Hoon Tong Ngin
2021 IEEE Access  
INDEX TERMS Control parameter adaptation, differential evolution, optimal reactive power dispatch. MING NIU received the B.S. degree from the  ...  This paper proposes a novel adaptive range composite differential evolution (ARCoDE) algorithm to efficiently and accurately solve optimal reactive power dispatch (ORPD) problem.  ...  from the top 3 ranking algorithms in the IEEE Competition on ''Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problem'', i.e. improved (µ + λ)-constrained differential  ... 
doi:10.1109/access.2021.3053640 fatcat:hjkm7g7atzgjhdrja567q55vfi

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example [chapter]

Jana Doering, Angel A. Juan, Renatas Kizys, Angels Fito, Laura Calvet
2016 Lecture Notes in Business Information Processing  
In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem  ...  This paper reviews the scientific literature on the use of metaheuristics for solving rich versions of the POP and illustrates, with a numerical example, the capacity of these methods to provide high-quality  ...  Acknowledgments This work has been partially supported with doctoral grants from the UOC, the Spanish Ministry of Economy and Competitiveness (grants TRA2013-48180-C3-3-P, TRA2015-71883-REDT) and FEDER  ... 
doi:10.1007/978-3-319-40506-3_3 fatcat:3ijtfa46avgqlaztnntouhypmy

An improved grey prediction evolution algorithm based on topological opposition-based learning

Canyun Dai, Zhongbo Hu, Zheng Li, Zenggang Xiong, Qinghua Su
2020 IEEE Access  
The grey prediction evolution algorithm based on the even grey model (GPEAe) proposed by Z.B.Hu et al. in 2019 is a competitively stochastic real-parameter optimization algorithm with characters of simple  ...  To improve the algorithmic overall performance, a topological opposition-based learning strategy (TOBL) is first developed to enhance its exploitation capability in this paper.  ...  This problem is solved by differential evolution with dynamic stochastic selection (DEDS) [54] , hybrid evolution algorithm (HEAA) [55] , hybrid particle swarm optimization with differential evolution  ... 
doi:10.1109/access.2020.2973197 fatcat:6obqpe7l4vbzvhk4rqmxjgxpnu

A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Zhongbo Hu, Shengwu Xiong, Zhixiang Fang, Qinghua Su
2014 Mathematical Problems in Engineering  
Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago.  ...  The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.  ...  Introduction Differential evolution (DE) is a population-based stochastic real-parameter algorithm for continuous optimization problems firstly introduced by [1, 2] .  ... 
doi:10.1155/2014/135652 fatcat:cwqjtdly2nbcnkafglppppfype
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