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Comparison between Genetic Algorithms and Particle Swarm Optimization

Comparison between Genetic Algorithms and Particle Swarm Optimization,10.1007/BFb0040812,Russell C. Eberhart,Yuhui Shi

Comparison between Genetic Algorithms and Particle Swarm Optimization   (Citations: 394)
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This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.
Conference: Evolutionary Programming , pp. 611-616, 1998
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    • ...The PSO technique can generate a high-quality solution with short calculation time and a more stable convergence characteristic compared to other evolutionary methods (Eberhart and Shi 1998, Yoshida et al...

    M. J. Mahmoodabadiet al. A new optimization algorithm based on a combination of particle swarm ...

    • ...gif"/> are acceleration constants (Eberhart and Shi 1998); w is the inertia weight (Eberhart and Shi 2000)...

    Kit Yan Chanet al. Handling uncertainties in modelling manufacturing processes with hybri...

    • ...However, PSO searches for an optimum through each particle flying in the search space and adjusting its flying trajectory according to its personal best experience and its neighborhood’s best experience rather than through particles undergoing genetic operations like selection, crossover, and mutation [5]...
    • ...The salient feature of PSO lies in its learning mechanism that distinguishes the algorithm from other EC techniques [5]...
    • ...For example, given a 3-dimension Sphere functionf(X )= x 2 + x 2 + x 2 , whose global minimum point is [0, 0, 0]. Suppose that the current position is Xi = [2, 5, 2], its personal best position is Pi = [0, 2, 5] and its neighborhood’s best position is Pn = [5, 0, 1]. The updated velocity is Vi = [1, −8, 2] according to (5), and thus the new position is Xi = Xi + Vi = [3, −3, 4], resulting in a new position with a cost value of 34 which is ...
    • ...For example, given a 3-dimension Sphere functionf(X )= x 2 + x 2 + x 2 , whose global minimum point is [0, 0, 0]. Suppose that the current position is Xi = [2, 5, 2], its personal best position is Pi = [0, 2, 5] and its neighborhood’s best position is Pn = [5, 0, 1]. The updated velocity is Vi = [1, −8, 2] according to (5), and thus the new position is Xi = Xi + Vi = [3, −3, 4], resulting in a new position with a cost value of 34 which is ...
    • ...For example, given a 3-dimension Sphere functionf(X )= x 2 + x 2 + x 2 , whose global minimum point is [0, 0, 0]. Suppose that the current position is Xi = [2, 5, 2], its personal best position is Pi = [0, 2, 5] and its neighborhood’s best position is Pn = [5, 0, 1]. The updated velocity is Vi = [1, −8, 2] according to (5), and thus the new position is Xi = Xi + Vi = [3, −3, 4], resulting in a new position with a cost value of 34 which is ...
    • ...Given the guidance of Po, the updated velocity become Vi = Po − Xi = [0, 0, 1] − [2, 5, 2] = [−2, −5, −1]; thus the new position is Xi = Xi + Vi = [0, 0, 1], resulting in a new and better position with a cost f(Xi) = 1 that makes the particle fly faster toward the global optimum [0, 0, 0]...

    Zhi-Hui Zhanet al. Orthogonal Learning Particle Swarm Optimization

    • ...SI algorithms share many common characteristics with EAs and are also regarded to be in the EC algorithm family [4]...

    Jun Zhanget al. Evolutionary Computation Meets Machine Learning: A Survey

    • ...The best previous position of an individual is recorded so far from the previous generation and is represented as ; the position of the best individual among all the individuals is represented as ; returns a uniform random number in the range of ; is an inertia weight factor; and are acceleration constants [16]...

    Kit Yan Chanet al. Modeling of a Liquid Epoxy Molding Process Using a Particle Swarm Opti...

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