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A New Cooperative Co-evolution Algorithm Based on Variable Grouping and Local Search for Large Scale Global Optimization

Shiwei Guan, Yuping Wang, Haiyan Liu
2017 Online) Taiwan Ubiquitous Information   unpublished
Many evolutionary algorithms have been proposed for large scale global optimization , but there are still many difficulties when handling high-dimensional optimization problems because of the search space  ...  First, a formula based grouping strategy is adopted to classify the interacting variables into the same subcomponent, and all of the subcomponents are optimized with a Modified SaNSDE (M-SaNSDE) and Modified  ...  This work was supported by National Natural Science Foundation of China (No. 61472297 and U1404622).  ... 
fatcat:ii7vzuw3jbdb5f36z2ypwliphe

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Atinesh Singh, Nanda Dulal
2017 International Journal of Computer Applications  
Broadly these algorithms can be categorized in 3 groups: Standard Evolutionary Algorithms, Cooperative Co-evolution (CC) based Evolutionary Algorithms and Memetic Algorithms.  ...  In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems.  ...  A new DMS-PSO, incorporated with a quasi-newton method, is proposed here [37] for Large Scale Global Optimization problems. This quasi-newton method improves the local search ability of DMS-PSO.  ... 
doi:10.5120/ijca2017914839 fatcat:2lhciqf4lbgetpyeouf5xykps4

Evolutionary large-scale global optimization

Mohammad Nabi Omidvar, Xiaodong Li
2017 Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '17  
Acknowledgement Thanks goes to Professor Xin Yao and EPSRC (grant nos. EP/K001523/1 and EP/J017515/1) for supporting this tutorial.  ...  "Cooperative co-evolution with differential grouping for large scale optimization".  ...  "MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization". In: IEEE Congress on Evolutionary Computation. IEEE. 2010, pp. 1-8.  ... 
doi:10.1145/3067695.3067706 dblp:conf/gecco/OmidvarL17 fatcat:r233d47vwjad3a6iokzunts75q

A Solution for Large Scale Optimization Problems Based on Gravitational Search Algorithm [article]

Somayeh Seifi Shalamzari
2021 arXiv   pre-print
This paper proposes a novel method for optimizing large scale problems by improving the gravitational search algorithm's performance.  ...  Heuristic algorithms, such as Gravitational Search Algorithm, are among the tools proposed for optimizing large-scale problems, but in this case, they cannot solve the problem on their own.  ...  Cooperative Co-evolutionary (CC) Method The Cooperative Co-evolutionary method is a new method based on and inspired by the divide-andconquer method, in which a large scale problem is divided into smaller  ... 
arXiv:2101.06630v1 fatcat:dbfbmdibabcllhfopw27j45iiy

A review of population-based metaheuristics for large-scale black-box global optimization: Part A

Mohammad Nabi Omidvar, Xiaodong Li, Xin Yao
2021 IEEE Transactions on Evolutionary Computation  
Part A of the series covers two major algorithmic approaches to large-scale global optimization: problem decomposition and memetic algorithms.  ...  The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly population-based metaheuristics.  ...  Hybrid local search and memetic algorithms play an important role in large-scale global optimization.  ... 
doi:10.1109/tevc.2021.3130838 fatcat:r24dalhsyjgg5es447bi5qmnbe

Smoothing and auxiliary functions based cooperative coevolution for global optimization

Fei Wei, Yuping Wang, Yuanliang Huo
2013 2013 IEEE Congress on Evolutionary Computation  
In this paper, a novel evolutionary algorithm framework called smoothing and auxiliary functions based cooperative coevolution (Briefly, SACC) for large scale global optimization problems is proposed.  ...  In this new algorithm pattern, a smoothing function and an auxiliary function are well integrated with a cooperative coevolution algorithm.  ...  Yao, "Cooperative co-evolution with delta grouping for large scale non-separable function optimization," in Proc.  ... 
doi:10.1109/cec.2013.6557900 dblp:conf/cec/WeiWH13 fatcat:5nb2l6ejcrhd5pgvlffxdtdv7y

Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records

Wei-Ping Ding, Chin-Teng Lin, Mukesh Prasad, Sen-Bo Chen, Zhi-Jin Guan
2016 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed  ...  Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data.  ...  Pal in the Electronics and Communication Science Unit of the Indian Statistical Institute, for providing very helpful suggestions, and also would like to thank Mr. Paul D.  ... 
doi:10.1109/tsmc.2015.2464787 fatcat:hguwegjfx5b2njeg4h7xdrwvau

A review of population-based metaheuristics for large-scale black-box global optimization: Part B

Mohammad Nabi Omidvar, Xiaodong Li, Xin Yao
2021 IEEE Transactions on Evolutionary Computation  
This paper is the second part of a two-part survey series on large-scale global optimization.  ...  The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search.  ...  The local search component of the algorithm is done using a divide-and-conquer strategy by performing local search for a subset of the decision variables.  ... 
doi:10.1109/tevc.2021.3130835 fatcat:3x5vho5kxfg4tc7zf4zd4c3crm

Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives

A N M Bazlur Rashid, Tonmoy Choudhury
2019 Problems and Perspectives in Management  
Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems.  ...  This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future  ...  A correlation-based memetic algorithm (MA) (GA plus a local search) FS tech- nique uses the symmetrical uncertainty for large- scale gene expression datasets (Kannan & Ramaraj, 2010).  ... 
doi:10.21511/ppm.17(4).2019.28 fatcat:76yr472o6rf7vm3torvgnxfcnm

A novel approach to minimum attribute reduction based on quantum-inspired self-adaptive cooperative co-evolution

Weiping Ding, Jiandong Wang
2013 Knowledge-Based Systems  
In this paper, a novel and efficient minimum attribute reduction algorithm based on quantum-inspired self-adaptive cooperative co-evolution incorporated into shuffled frog leaping algorithm is proposed  ...  Wang, A novel approach to minimum attribute reduction based on quantum-inspired self-adaptive cooperative co-evolution, Knowl. Based Syst. (2013), http://dx.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their insightful comments and suggestions which greatly improve the quality of this paper.  ... 
doi:10.1016/j.knosys.2013.03.008 fatcat:vzojhizr4rgfbgccyzetifnq6q

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews [chapter]

Ajith Abraham, Crina Grosan
2008 Studies in Computational Intelligence  
In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has  ...  uncertainty and vagueness.  ...  Authors explored a method that optimizes the architecture and initial weights of multilayer perceptrons, a search algorithm for training algorithm parameters, and finally, a co-evolutionary algorithm,  ... 
doi:10.1007/978-3-540-75396-4_1 fatcat:xplkbxzk4vbpbmnp4zxrjomj3q

Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

Mohammad Nabi Omidvar, Xiaodong Li, Yi Mei, Xin Yao
2014 IEEE Transactions on Evolutionary Computation  
Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm.  ...  In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems.  ...  Tang and W. Chen for providing the source code of the CCVIL algorithm and also for their valuable comments. They would also like to thank K. Qin, J. Harland, V. Ciesielski, and M.  ... 
doi:10.1109/tevc.2013.2281543 fatcat:ppelpl3bbnf2tpvtfqlpgbzlqa

Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization

Xingguang Peng, Yapei Wu
2018 Complexity  
This feature has great potential for large-scale global optimization (LSGO) while inducing some inherent problems of CC if a problem is improperly decomposed.  ...  The cooperative coevolution (CC) algorithm features a "divide-and-conquer" problem-solving process.  ...  Acknowledgments This work has been supported by the National Natural Science Foundation of China (nos. 6117235 and 61473233).  ... 
doi:10.1155/2018/9267054 fatcat:pjlcz4bzvnas7cgf6mlmxtzspm

Distributed contribution-based quantum-behaved particle swarm optimization with controlled diversity for large-scale global optimization problems

Qidong Chen, Jun Sun, Vasile Palade
2019 IEEE Access  
Many cooperative coevolution optimization algorithms have been proposed recently for solving large-scale global optimization problems.  ...  These algorithms first decompose a large-scale global optimization problem into several subproblems, each with a specific number of decision variables, and then optimize the subproblems separately.  ...  The delta grouping method generally outperforms the RG method when it is applied with difference evolution cooperative co-evolution (DECC) to the CEC2010 large-scale benchmark suites.  ... 
doi:10.1109/access.2019.2944196 fatcat:yvaqlhsqk5gshfen4ucn44apca

Smart use of computational resources based on contribution for cooperative co-evolutionary algorithms

Mohammad Nabi Omidvar, Xiaodong Li, Xin Yao
2011 Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11  
In this paper, we propose a Contribution Based Cooperative Co-evolution (CBCC) that selects the subcomponents based on their contributions to the global fitness.  ...  Standard Cooperative Co-evolution uses a round-robin method to select subcomponents to undergo optimization.  ...  Contribution Based Cooperative Co-evolution relies on a grouping strategy that captures the non-separable variables in a common subcomponents.  ... 
doi:10.1145/2001576.2001727 dblp:conf/gecco/OmidvarLY11 fatcat:j62rgzl3mbf6tgup2trrlx7iy4
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