Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of\nmultidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on\nL´evy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior\npatterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, L´evy flight is introduced into the searching\nstrategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered. Finally, the proposed\nalgorithm LFFSA is validated with several benchmark problems. Numerical results demonstrate that LFFSA has a better performance\nin convergence speed and optimization accuracy than the other test algorithms.
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