Abstract
Abstract
In this paper, we proposed an improvement for the newly raised swarm-based algorithm called the slime mould algorithm (SMA) with chaos. The so-called chaotic SMA introduced the specific Chebyshev mapping, which had already been verified to perform better in optimization. Three types of simulation experiments were carried out with the unimodal, multi-modal benchmark functions and those which have basins/valleys in their profiles. In order to reduce the influence of randomness involved in the algorithms, 100 Monte Carlo experiments were carried out and the final results were their averages. Results confirmed the capability of the improvements and demonstrated that the chaotic SMA with Chebyshev map would perform better, steadier, and faster than the original one in optimization. Discussions on the capability in optimization of the chaotic SMA together with the original SMA were made, and the chaotic SMA was recommended in applications for real engineering problems.
Subject
General Physics and Astronomy
Reference8 articles.
1. A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems;Lin;Journal of Computer and Information Technology,2012
2. Chaotic grey wolf optimization algorithm for constrained optimization problems;Mehak;Journal of Computational Design and Engineering,2018
3. Firefly algorithm with chaos;Gandomi;Communications in Nonlinear Science and Numerical Simulation,2013
4. Slime mould algorithm: A new method for stochastic optimization;Shimin;Future Generation Computer Systems,2020
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