Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
A group search optimizer for neural network training
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
IEEE Computational Intelligence Magazine
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Using QIGSO with steepest gradient descent strategy to direct orbits of chaotic systems
International Journal of Computational Science and Engineering
Time-varying social emotional optimisation algorithm
International Journal of Computing Science and Mathematics
Group search optimiser: a brief survey
International Journal of Computing Science and Mathematics
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Group Search Optimiser (GSO) is a new swarm intelligence optimiser algorithm inspired by animal social behaviours. In this paper, we proposed two variants of GSO to improve its search capability, and applied them to solving non-linear equations. Producers in the GSO are like the eyes of animals, which determine the "ï戮聵food' position and the movement direction of scroungers. However, due to the random sample mechanism, the computational efficiency is poor. To improve its search efficiency, a new GSO based on quadratic interpolation method (QIGSO) is proposed, in which the estimated position with quadratic interpolation theory is used to replace the random point in each iteration to increase the speed of convergence. Furthermore, the steepest gradient descent method is also incorporated into QIGSO (QIGSO_SDO) to improve the local search capability. Numerical simulation and a special problem about radar detection are used to test the proposed two variants.