Hybrid Metaheuristic Optimization Approach: Theory and its Application

Mathematical optimization or mathematical programming is the selection of the best element from some set of available alternatives. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries. In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.

[1]  Erwie Zahara,et al.  A hybridized approach to data clustering , 2008, Expert Syst. Appl..

[2]  Yong Zhang,et al.  A PSO-based multi-objective multi-label feature selection method in classification , 2017, Scientific Reports.

[3]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[4]  Vijay Kumar,et al.  Grey Wolf Algorithm-Based Clustering Technique , 2017, J. Intell. Syst..

[5]  Xiangtao Li,et al.  Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification , 2019, IEEE Transactions on Cybernetics.

[6]  Latha Parthiban,et al.  Multi Objective Fractional Cuckoo Search for Data Clustering and Its Application to Medical Field , 2015 .

[7]  J. Sil,et al.  Simultaneous continuous feature selection and K clustering by Multi Objective Genetic Algorithm , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[8]  Roberto Battiti,et al.  The Reactive Tabu Search , 1994, INFORMS J. Comput..

[9]  Farid Melgani,et al.  Clustering of Hyperspectral Images Based on Multiobjective Particle Swarm Optimization , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Ajith Abraham,et al.  Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution , 2009, HAIS.

[11]  V. Pareto,et al.  Vilfredo Pareto. Cours d’Économie Politique , 1897 .

[12]  Ujjwal Maulik,et al.  Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Joshua D. Knowles,et al.  Evolutionary Multiobjective Clustering , 2004, PPSN.

[14]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[15]  Adam Baharum,et al.  Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing , 2015, PloS one.

[16]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[17]  Manoj Kumar Tiwari,et al.  Swarm Intelligence, Focus on Ant and Particle Swarm Optimization , 2007 .

[18]  Amit Konar,et al.  Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm , 2008, Pattern Recognit. Lett..

[19]  Ali Karci Imitation of Bee Reproduction as a Crossover Operator in Genetic Algorithms , 2004, PRICAI.

[20]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[21]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[22]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[23]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[24]  Kumara Sastry Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability , 2007 .

[25]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[26]  Sriparna Saha,et al.  A generalized automatic clustering algorithm in a multiobjective framework , 2013, Appl. Soft Comput..

[27]  S. Bandyopadhyay,et al.  Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes , 2009, BMC Bioinformatics.

[28]  Ajith Abraham,et al.  Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis , 2009, Sensors.

[29]  Sanghamitra Bandyopadhyay,et al.  A new multiobjective simulated annealing based clustering technique using symmetry , 2009, Pattern Recognit. Lett..

[30]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[31]  Ana L. C. Bazzan,et al.  A Multiagent, Multiobjective Clustering Algorithm , 2009, Data Mining and Multi-agent Integration.

[32]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[33]  Longbing Cao,et al.  Multiobjective evolutionary algorithm-based soft subspace clustering , 2012, 2012 IEEE Congress on Evolutionary Computation.

[34]  M. Tahar Kechadi,et al.  Automatic multi-objective clustering based on game theory , 2017, Expert Syst. Appl..

[35]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[36]  Asif Ekbal,et al.  Feature Selection and Semi-supervised Clustering Using Multiobjective Optimization , 2014, SOCO 2014.

[37]  Joshua D. Knowles,et al.  Multiobjective clustering around medoids , 2005, 2005 IEEE Congress on Evolutionary Computation.

[38]  Ujjwal Maulik,et al.  A multiobjective approach to MR brain image segmentation , 2011, Appl. Soft Comput..

[39]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[40]  Martin J. Oates,et al.  PESA-II: region-based selection in evolutionary multiobjective optimization , 2001 .

[41]  Fernando Jiménez,et al.  Multi-objective evolutionary feature selection for online sales forecasting , 2017, Neurocomputing.

[42]  Edwin Lughofer,et al.  Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives , 2013, Eng. Appl. Artif. Intell..

[43]  Reynold Cheng,et al.  Uncertain Data Mining: A New Research Direction , 2005 .

[44]  Li-Chen Fu,et al.  ptimization Algorithm for Multi-o ring in Mobile Ad Hoc Networks , 2014 .

[45]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .

[46]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[47]  Joshua D. Knowles,et al.  An Evolutionary Approach to Multiobjective Clustering , 2007, IEEE Transactions on Evolutionary Computation.

[48]  Jaya Sil,et al.  Simultaneous feature selection and clustering with mixed features by multi objective genetic algorithm , 2014, Int. J. Hybrid Intell. Syst..

[49]  Haisong Chen,et al.  Improved multi-objective clustering algorithm using particle swarm optimization , 2017, PloS one.

[50]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[51]  Eckart Zitzler,et al.  Evolutionary multi-objective optimization , 2007, Eur. J. Oper. Res..

[52]  Ganapati Panda,et al.  Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models , 2013, Eng. Appl. Artif. Intell..

[53]  Chee Peng Lim,et al.  A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models , 2014, Neurocomputing.

[54]  Saptarsi Goswami,et al.  Multi-objective Genetic Algorithm setup for feature subset selection in clustering , 2016, 2016 3rd International Conference on Recent Advances in Information Technology (RAIT).

[55]  A. Hadi,et al.  Improving parameter estimation using constrained optimization methods , 2012 .

[56]  Edwin Lughofer,et al.  DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm , 2014, Soft Computing.

[57]  Mengjie Zhang,et al.  Multi-objective particle swarm optimisation (PSO) for feature selection , 2012, GECCO '12.

[58]  Yangyang Li,et al.  Multi-objective Invasive Weed Optimization algortihm for clustering , 2012, 2012 IEEE Congress on Evolutionary Computation.

[59]  Sung Hoon Jung,et al.  Queen-bee evolution for genetic algorithms , 2003 .

[60]  Amit Konar,et al.  Swarm Intelligence Algorithms in Bioinformatics , 2008, Computational Intelligence in Bioinformatics.

[61]  Farrukh Aslam Khan,et al.  Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization , 2012, Appl. Soft Comput..

[62]  Yue Zhang,et al.  BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior , 2004, ANTS Workshop.

[63]  Jacob Scharcanski,et al.  Feature selection for face recognition based on multi-objective evolutionary wrappers , 2013, Expert Syst. Appl..

[64]  Sanghamitra Bandyopadhyay,et al.  A symmetry based multiobjective clustering technique for automatic evolution of clusters , 2010, Pattern Recognit..

[65]  M. Tahar Kechadi,et al.  Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications , 2010, Expert Syst. Appl..

[66]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .