Recent approaches to global optimization problems through Particle Swarm Optimization
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[1] E. O. Roxin. Optimization Theory and Applications (Lamberto Cesari) , 1984 .
[2] Elijah Polak,et al. Optimization: Algorithms and Consistent Approximations , 1997 .
[3] Hans-Paul Schwefel,et al. Numerical Optimization of Computer Models , 1982 .
[4] Eckart Zitzler,et al. Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .
[5] V. F. Demʹi︠a︡nov,et al. Introduction to minimax , 1976 .
[6] A. V. Levy,et al. Topics in global optimization , 1982 .
[7] M. N. Vrahatis,et al. A New Unconstrained Optimization Method for Imprecise Function and Gradient Values , 1996 .
[8] Peter J. Angeline,et al. Tracking Extrema in Dynamic Environments , 1997, Evolutionary Programming.
[9] David B. Fogel,et al. Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .
[10] Ralf Salomon,et al. Evolutionary algorithms and gradient search: similarities and differences , 1998, IEEE Trans. Evol. Comput..
[11] Singiresu S. Rao. Engineering Optimization : Theory and Practice , 2010 .
[12] Hans-Paul Schwefel,et al. Evolution and optimum seeking , 1995, Sixth-generation computer technology series.
[13] Günter Rudolph,et al. An Evolutionary Algorithm for Integer Programming , 1994, PPSN.
[14] G. A. Watson,et al. On the solution of the errors in variables problem using thel1 norm , 1991 .
[15] K. Parsopoulos,et al. Stretching technique for obtaining global minimizers through Particle Swarm Optimization , 2001 .
[16] Xing-Si Li,et al. AN AGGREGATE FUNCTION METHOD FOR NONLINEAR PROGRAMMING , 1991 .
[17] R. W. Dobbins,et al. Computational intelligence PC tools , 1996 .
[18] W. Murray,et al. A Projected Lagrangian Algorithm for Nonlinear Minimax Optimization , 1980 .
[19] John E. Dennis,et al. Multidirectional search: a direct search algorithm for parallel machines , 1989 .
[20] Leon S. Lasdon,et al. Optimization in engineering design , 1967 .
[21] Ji-Ming Peng,et al. A non-interior continuation method for generalized linear complementarity problems , 1999, Math. Program..
[22] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.
[23] Sandro Ridella,et al. Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.
[24] William T. Reeves. Particle systems—a technique for modeling a class of fuzzy objects , 1993 .
[25] Nikolaus Hansen,et al. Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie , 1998 .
[26] V. J. Torczoit,et al. Multidirectional search: a direct search algorithm for parallel machines , 1989 .
[27] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[28] E. Polak. On the mathematical foundations of nondifferentiable optimization in engineering design , 1987 .
[29] P. Pardalos,et al. Minimax and applications , 1995 .
[30] Hans-Paul Schwefel,et al. Evolution strategies – A comprehensive introduction , 2002, Natural Computing.
[31] Russell C. Eberhart,et al. Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.
[32] M. N. Vrahatis,et al. Particle swarm optimization method in multiobjective problems , 2002, SAC '02.
[33] Michael N. Vrahatis,et al. Particle swarm optimization for minimax problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[34] H. Beyer. Evolutionary algorithms in noisy environments : theoretical issues and guidelines for practice , 2000 .
[35] Hans-Georg Beyer,et al. The Theory of Evolution Strategies , 2001, Natural Computing Series.
[36] E. T. Jaynes,et al. Where do we Stand on Maximum Entropy , 1979 .
[37] R. Hodgson,et al. Genetic Algorithm Approach to Particle Identification by Light Scattering. , 2000, Journal of colloid and interface science.
[38] J. Spall. Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .
[39] D. E. Goldberg,et al. Genetic Algorithms in Search , 1989 .
[40] Russell C. Eberhart,et al. Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.
[41] Jorge Nocedal,et al. Theory of algorithms for unconstrained optimization , 1992, Acta Numerica.
[42] Michael N. Vrahatis,et al. Computing with certainty individual members of families of periodic orbits of a given period , 2001 .
[43] M. E. Muller,et al. A Note on the Generation of Random Normal Deviates , 1958 .
[44] Thomas Bäck,et al. Evolutionary Algorithms in Theory and Practice , 1996 .
[45] Robert Hooke,et al. `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.
[46] Jorge J. Moré,et al. Testing Unconstrained Optimization Software , 1981, TOMS.
[47] A. Skinner,et al. Neural networks in computational materials science: training algorithms , 1995 .
[48] Jean-Baptiste Hiriart-Urruty,et al. On optimality conditions in nondifferentiable programming , 1978, Math. Program..
[49] D A Pierre,et al. Optimization Theory with Applications , 1986 .
[50] Marimuthu Palaniswami,et al. Computational Intelligence: A Dynamic System Perspective , 1995 .
[51] Carl Tim Kelley,et al. Iterative methods for optimization , 1999, Frontiers in applied mathematics.
[52] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
[53] Thomas Bäck,et al. Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .
[54] Lothar Thiele,et al. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.
[55] J. Snyman,et al. A multi-start global minimization algorithm with dynamic search trajectories , 1987 .
[56] Craig W. Reynolds. Flocks, herds, and schools: a distributed behavioral model , 1998 .
[57] Saul Krasner,et al. The Ubiquity of chaos , 1990 .
[58] Song Xu,et al. A non–interior predictor–corrector path following algorithm for the monotone linear complementarity problem , 2000, Math. Program..
[59] Wolfgang Banzhaf,et al. Genetic Programming: An Introduction , 1997 .
[60] R. Fletcher. Practical Methods of Optimization , 1988 .
[61] R. Kelahan,et al. Application of the adaptive random search to discrete and mixed integer optimization , 1978 .
[62] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[63] E. K. Blum,et al. Approximation of Boolean Functions by Sigmoidal Networks: Part I: XOR and Other Two-Variable Functions , 1989, Neural Computation.
[64] C. Charalambous,et al. Nonlinear programming using minimax techniques , 1974 .
[65] G. Alistair Watson,et al. An Algorithm for Minimax Approximation in the Nonlinear Case , 1969, Comput. J..
[66] P. Pardalos,et al. Handbook of global optimization , 1995 .
[67] Konstantinos E. Parsopoulos,et al. PARTICLE SWARM OPTIMIZER IN NOISY AND CONTINUOUSLY CHANGING ENVIRONMENTS , 2001 .
[68] Yaochu Jin,et al. Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how? , 2001 .
[69] G. A. Watson,et al. Choice of norms for data fitting and function approximation , 1998, Acta Numerica.
[70] Michael N. Vrahatis,et al. Particle swarm optimization for integer programming , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[71] D. Bertsekas,et al. A new penalty function method for constrained minimization , 1972, CDC 1972.
[72] W. Paszkowicz,et al. Application of the Smooth Genetic Algorithm for Indexing Powder Patterns – Tests for the Orthorhombic System , 1996 .
[73] János D. Pintér,et al. Global optimization in action , 1995 .
[74] Michael N. Vrahatis,et al. Artificial nonmonotonic neural networks , 2001, Artif. Intell..
[75] D. Bertsekas. Approximation procedures based on the method of multipliers , 1977 .
[76] Drossos,et al. Method for computing long periodic orbits of dynamical systems. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[77] M. N. Vrahatis,et al. Objective function “stretching” to alleviate convergence to local minima , 2001 .
[78] Aimo A. Törn,et al. Global Optimization , 1999, Science.
[79] Zbigniew Michalewicz,et al. Handbook of Evolutionary Computation , 1997 .
[80] Peter Nordin,et al. Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .
[81] Francisco Facchinei,et al. A smoothing method for mathematical programs with equilibrium constraints , 1999, Math. Program..
[82] A. Neumaier,et al. A grid algorithm for bound constrained optimization of noisy functions , 1995 .
[83] Zbigniew Michalewicz,et al. Genetic Algorithms Plus Data Structures Equals Evolution Programs , 1994 .
[84] R. Eberhart,et al. Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).
[85] A. Conn,et al. An Efficient Method to Solve the Minimax Problem Directly , 1978 .
[86] Song Xu,et al. Smoothing Method for Minimax Problems , 2001, Comput. Optim. Appl..
[87] V. F. Demʹi︠a︡nov,et al. Introduction to minimax , 1976 .
[88] Virginia Torczon,et al. On the Convergence of the Multidirectional Search Algorithm , 1991, SIAM J. Optim..
[89] Michael N. Vrahatis,et al. Application of the Characteristic Bisection Method for locating and computing periodic orbits in molecular systems , 2001 .
[90] H. Britt,et al. The Estimation of Parameters in Nonlinear, Implicit Models , 1973 .
[91] Peter J. Angeline,et al. Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.
[92] M. Fukushima,et al. Minimizing multimodal functions by simplex coding genetic algorithm , 2003 .
[93] Günter Rudolph,et al. Contemporary Evolution Strategies , 1995, ECAL.
[94] James Kennedy,et al. Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[95] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[96] James C. Spall,et al. AN OVERVIEW OF THE SIMULTANEOUS PERTURBATION METHOD FOR EFFICIENT OPTIMIZATION , 1998 .
[97] Hans-Paul Schwefel,et al. Evolution and Optimum Seeking: The Sixth Generation , 1993 .
[98] Ulf Grenander,et al. A stochastic nonlinear model for coordinated bird flocks , 1990 .
[99] E. Wilson,et al. Sociobiology: The New Synthesis , 1975 .
[100] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[101] Mark M. Millonas,et al. Swarms, Phase Transitions, and Collective Intelligence , 1993, adap-org/9306002.
[102] G. Hogg,et al. UNCONSTRAINED DISCRETE NONLINEAR PROGRAMMING , 1979 .
[103] Dimitri P. Bertsekas,et al. A new algorithm for solution of resistive networks involving diodes , 1976 .
[104] Timothy S Bush,et al. Evolutionary programming techniques for predicting inorganic crystal structures , 1995 .
[105] James Kennedy,et al. The Behavior of Particles , 1998, Evolutionary Programming.
[106] R. Horst,et al. Global Optimization: Deterministic Approaches , 1992 .
[107] M. N. Vrahatisa,et al. A class of gradient unconstrained minimization algorithms with adaptive stepsize , 1999 .
[108] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[109] J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .
[110] M. Powell. A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation , 1994 .
[111] Michael N. Vrahatis,et al. Modification of the Particle Swarm Optimizer for Locating All the Global Minima , 2001 .
[112] Vijay K. Garg,et al. A genetic algorithm for fitting Lorentzian line shapes in Mössbauer spectra , 1997 .
[113] A. Neumaier,et al. Solving minimax problems by interval methods , 1990 .
[114] David W. Corne,et al. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.
[115] Vassilis P. Plagianakos,et al. Training neural networks with threshold activation functions and constrained integer weights , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[116] Z. Kam,et al. Absorption and Scattering of Light by Small Particles , 1998 .
[117] Vassilis P. Plagianakos,et al. Improving the Particle Swarm Optimizer by Function “Stretching” , 2001 .
[118] Eldon Hansen,et al. Global optimization using interval analysis , 1992, Pure and applied mathematics.
[119] Yue Shi,et al. A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[120] Michael N. Vrahatis,et al. PARTICLE SWARM OPTIMIZATION FOR IMPRECISE PROBLEMS , 2002 .
[121] G. Alistair Watson,et al. The use of the L 1 norm in nonlinear errors-in-variables problems , 1997 .
[122] C. T. Kelley,et al. An Implicit Filtering Algorithm for Optimization of Functions with Many Local Minima , 1995, SIAM J. Optim..
[123] Sam Kwong,et al. Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..