PARSOPOULOS, K. E., AND VRAHATIS, M. N. Particle swarm optimizer in noisy and continuously changing environments. Artificial Intelligence and Soft Computing (2001), 289--294. Home/Search Document Details and Download
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Particle Swarm Optimization for Integer Programming - Laskari, Parsopoulos, Vrahatis (2002) (1 citation) Self-citation (Parsopoulos Vrahatis) (Correct)
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K.E. Parsopoulos and M.N. Vrahatis, "Particle Swarm Optimizer in Noisy and Continuously Changing Environments", M.H. Hamza (Ed.), Artificial Intelligence and Soft Computing, IASTED/ACTA Press, pp. 289--294, 2001.
Particle Swarm Optimization for Minimax Problems - Laskari, Parsopoulos, Vrahatis (2002) Self-citation (Parsopoulos Vrahatis) (Correct)
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K.E. Parsopoulos and M.N. Vrahatis, "Particle Swarm Optimizer in Noisy and Continuously Changing Environments", M.H. Hamza (Ed.), Artificial Intelligence and Soft Computing, IASTED/ACTA Press, pp. 289--294, 2001.
Particle Swarm Optimization for Imprecise Problems - Parsopoulos, Vrahatis (2001) Self-citation (Parsopoulos Vrahatis) (Correct)
....but also indispensable. Moreover, in many applications there are imprecise values for the input data as well as for the function values. Therefore, the development of robust and efficient GO methods for noisy environments, such as the aforementioned is a subject of considerable ongoing research [7, 18, 23, 25]. The Particle Swarm Optimization (PSO) technique has been developed by Eberhart and Kennedy in 1995 [11] and it is a simple evolutionary algorithm which differs from other evolutionary computation techniques in that it is motivated from the simulation of social behavior. PSO exhibits good ....
....and Kennedy in 1995 [11] and it is a simple evolutionary algorithm which differs from other evolutionary computation techniques in that it is motivated from the simulation of social behavior. PSO exhibits good performance in finding solutions to static optimization problems [15, 16, 17] In [18] a first study of the performance of PSO in noisy and continuously changing environments has been presented. In the following paragraphs this study is extended and further experiments on well known test functions as well as for particle identification by light scattering are presented. The ....
K.E. Parsopoulos, M.N. Vrahatis, Particle Swarm Optimizer in noisy and continuously changing environments, Artificial Intelligence and Soft Computing, M.H. Hamza (Ed.), IASTED/ACTA Press, 289-294, 2001.
Computing Periodic Orbits of Nonlinear Mappings Through.. - Parsopoulos, Vrahatis (2002) Self-citation (Vrahatis) (Correct)
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Parsopoulos, K.E., Vrahatis, M.N. (2001), "Particle Swarm Optimizer in Noisy and Continuously Changing Environments", in M.H. Hamza (Ed.) Artificial Intelligence and Soft Computing, pp. 289-294, IASTED/ACTA Press.
Solving L1 Norm Errors-In-Variables Problems - Parsopoulos, Laskari, Vrahatis (2001) Self-citation (Parsopoulos Vrahatis) (Correct)
....from the swarm s flying experience. In another words, PSO is considered as performing mutation with a conscience , as pointed out by Eberhart and Shi [2] The PSO technique has been proved very efficient in solving general Global Optimization problems and performing Neural Networks training [9, 10, 11, 12]. In the next section, results obtained by the application of this technique to data fitting modelling problems are exhibited and conclusions are derived in the final section of the paper. 4. EXPERIMENTAL RESULTS The models that we consider in this section are (or assumed to be) implicit and are ....
K.E. Parsopoulos, M.N. Vrahatis, Particle Swarm Optimizer in noisy and continuously changing environments, in M.H. Hamza (Ed.), Artificial Intelligence and Soft Computing, (IASTED/ACTA Press, 2001), 289--294.
Particle Swarm Optimization Method in Multiobjective Problems - Parsopoulos, Vrahatis (2002) (6 citations) Self-citation (Parsopoulos Vrahatis) (Correct)
.... [20] The Particle Swarm Optimization (PSO) is a Swarm Intelligence method that models social behavior to guide swarms of particles towards the most promising regions of the search space [3] PSO has proved to be efficient at solving Unconstrained Global Optimization and engineering problems [4, 10, 11, 12, 13, 17]. It is easily implemented, using either binary or floating point encoding, and it usually results in faster convergence rates than the Genetic Algorithms [7] Although PSO s performance, in single objective optimization tasks, has been extensively studied, there are insufficient results for MO ....
K.E. Parsopoulos and M.N. Vrahatis. Particle Swarm Optimizer in Noisy and Continuously Changing Environments. M.H. Hamza (Ed.), Artificial Intelligence and Soft Computing, pages 289--294, IASTED/ACTA Press, 2001.
Noisy Optimization Problems - A Particular Challenge.. - Krink, Filipic.. (2004) (Correct)
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PARSOPOULOS, K. E., AND VRAHATIS, M. N. Particle swarm optimizer in noisy and continuously changing environments. Artificial Intelligence and Soft Computing (2001), 289--294.
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