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Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey
2016
International Journal of Advanced Computer Science and Applications
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based information, local search optimizers, multiple ensemble search operators together with self-adaptive strategies, metaheuristics, mating restriction approaches, statistical sampling techniques, integration ...
of Fuzzy dominance concepts and many other advanced techniques for dealing with diverse optimization and search problems ...
The original MOEA/D was then further enhanced in [101] by replacing simulated binary crossover (SBX) [81] with differential evolution (DE) [162] . ...
doi:10.14569/ijacsa.2016.070274
fatcat:3oleqyfntzdz5hwkd3f5df56qi
Multiobjective evolutionary algorithms: A survey of the state of the art
2011
Swarm and Evolutionary Computation
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It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs ...
for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems ...
Acknowledgements This work is partly supported by National Basic Research Program of China (No. 2011CB707104) and National Science Foundation of China (No. 61005050). ...
doi:10.1016/j.swevo.2011.03.001
fatcat:jfcghitjp5ap5he4d3ackhhjsu
Differential Evolution: A Survey of the State-of-the-Art
2011
IEEE Transactions on Evolutionary Computation
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Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. ...
Index Terms-Derivative-free optimization, differential evolution (DE), direct search, evolutionary algorithms (EAs), genetic algorithms (GAs), metaheuristics, particle swarm optimization (PSO). ...
neighborhood-based mutation [S150] Modified DE with local and global best mutation [S151], DE with random scale factor and time-varying crossover rate [S152]. ...
doi:10.1109/tevc.2010.2059031
fatcat:cqpj2kaqzbdtvoezqg77qigsgu
A Many-objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design
2020
IEEE Access
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[48] proposed multi-objective Differential Evolution (DE) algorithm with Pareto tournaments for the problem of sequence design of DNA. ...
Memetic algorithms [52] and differential evolution algorithms [53] , [54] have been used in past studies for other problems. ...
doi:10.1109/access.2020.3040752
fatcat:rbsw6xzcb5g5hmazqiieppk7nu
A hierarchical particle swarm optimizer with latin sampling based memetic algorithm for numerical optimization
2013
Applied Soft Computing
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In this paper, we propose a new memetic computing model, using a Hierarchical Particle Swarm Optimizer (HPSO) and Latin Hypercube Sampling (LHS) method. ...
The learning strategy for each swarm is the well-known Comprehensive Learning method with a newly designed mutation operator. ...
P.N.Suganthan for providing the source code of "CLPSO" . ...
doi:10.1016/j.asoc.2012.05.020
fatcat:grbkwuscf5d5niqfnslfddilnm
Differential evolution with adaptive guiding mechanism based on heuristic rules
2019
IEEE Access
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INDEX TERMS Differential evolution, adaptive guiding mechanism, heuristic rule, mutation operator, numerical optimization. ...
This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution ...
Note that MVMO-SH is a memetic EA that incorporates local search and multi-parent crossover strategies [64] . ...
doi:10.1109/access.2019.2914963
fatcat:t7cql5viarcpfnyp4cljnddmkq
Hybrid Multiobjective Evolutionary Algorithms in Small Molecule De Novo Design
2010
Zenodo
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Our research proposes, MEGA, an algorithmic framework for solving the problem of multi-objective optimal graph design for labeled, undirected graphs. ...
The method uses the multi-objective evolutionary graph, a graph-specific optimization metaheuristic that combines evolutionary algorithms with graph theory and local search techniques exploiting domain-specific ...
In a series of publications, Bryden strings with standard single-point crossover and bit-flip mutation used for evolution. ...
doi:10.5281/zenodo.2592431
fatcat:eolxugjcyzcspnxy2owmrjm7ei
Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy
2012
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely ...
mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. ...
DIFFERENTIAL EVOLUTION AND PARTICLE SWARM OPTIMIZATION
A. ...
doi:10.1109/tsmcc.2011.2160941
fatcat:msnoo2lmknaptlqt64woszmvoq
Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics
2009
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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metaheuristics: random multi-start local search, iterated local search, and variable neighborhood search. ...
Local genetic algorithms have been designed with the aim of providing effective intensification. ...
In particular, in Noman and Iba (2008) , an adaptive crossover hill-climbing operator, which adaptively adjusts the length of the refinement process, is proposed for Memetic Algorithms based on Differential ...
doi:10.1007/s00500-009-0506-1
fatcat:uwsyxmlygbgr3gkt7oax627pmy
Welcome message from the General Chairs
2009
2009 International Workshop on Satellite and Space Communications
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All accepted papers will be included in the Proceeding of Adaptation, Learning and Optimization Series published by Springer-Verlag. ...
Many papers demonstrated notable systems with good analytical and/or empirical analyses. Each paper was reviewed by at least two reviewers (most papers received three reviews each). ...
Keywords: Differential evolution algorithm, Adaptive parameter control, Population size, Mutation strategy, Global numerical optimization. ...
doi:10.1109/iwssc.2009.5286448
fatcat:wcu4uzasizhzjmdkzyekynnqwi
Differential Evolution in Wireless Communications: A Review
2019
International Journal of Online and Biomedical Engineering (iJOE)
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<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. ...
Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. ...
Acknowledgement The authors are grateful to Covenant University for providing an enabling environment for this research. ...
doi:10.3991/ijoe.v15i11.10651
fatcat:rd6l52wiuned7fv4epu3qtcbnq
Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives
2019
Problems and Perspectives in Management
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Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems. ...
Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. ...
Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization. ...
doi:10.21511/ppm.17(4).2019.28
fatcat:76yr472o6rf7vm3torvgnxfcnm
Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications
2017
IEEE Transactions on Evolutionary Computation
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Epitropakis serves as a Reviewer for numerous high-impact journals and first tier conferences. He is a Founding Member and the Chair of the IEEE CIS Task Force on Multi-Modal Optimization. . ...
Furthermore, the paper surveys previous attempts on leveraging the capabilities of niching to facilitate various optimization (e.g., multi-objective and dynamic optimization) and machine learning (e.g. ...
ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their constructive comments which have greatly improved the quality of this paper. ...
doi:10.1109/tevc.2016.2638437
fatcat:7ol4ecd35bfjvkn6mujx37uocu
A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis
2016
Applied Soft Computing
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All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed ...
Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. ...
the differential evolution-based memetic algorithm [136] . ...
doi:10.1016/j.asoc.2016.03.004
fatcat:3mgmo7vuurd4tnijod5a5kwn7m
A Preliminary Survey on Optimized Multiobjective Metaheuristic Methods for Data Clustering Using Evolutionary Approaches
2013
International Journal of Computer Science & Information Technology (IJCSIT)
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Finally, this study addresses the potential challenges of MOEA design and data clustering, along with conclusions and recommendations for novice and researchers by positioning most promising paths of future ...
The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective Evolutionary Approaches (MOEAs) methods. ...
Differential evolution DE [29] , [53] materialized as a straightforward and well-organized scheme for global optimization over continuous spaces more than a decade ago. ...
doi:10.5121/ijcsit.2013.5504
fatcat:4b2ye5jfqvhddf23gptdk3mh2y
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