An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm.

Engineering with Computers
Dinesh DhawalePriyanka Anand

Abstract

Slime mold algorithm (SMA) is a recently developed meta-heuristic algorithm that mimics the ability of a single-cell organism (slime mold) for finding the shortest paths between food centers to search or explore a better solution. It is noticed that entrapment in local minima is the most common problem of these meta-heuristic algorithms. Thus, to further enhance the exploitation phase of SMA, this paper introduces a novel chaotic algorithm in which sinusoidal chaotic function has been combined with the basic SMA. The resultant chaotic slime mold algorithm (CSMA) is applied to 23 extensively used standard test functions and 10 multidisciplinary design problems. To check the validity of the proposed algorithm, results of CSMA has been compared with other recently developed and well-known classical optimizers such as PSO, DE, SSA, MVO, GWO, DE, MFO, SCA, CS, TSA, PSO-DE, GA, HS, Ray and Sain, MBA, ACO, and MMA. Statistical results suggest that chaotic strategy facilitates SMA to provide better performance in terms of solution accuracy. The simulation result shows that the developed chaotic algorithm outperforms on almost all benchmark functions and multidisciplinary engineering design problems with superior convergence.

References

Nov 13, 2004·Proceedings. Biological Sciences·Toshiyuki NakagakiTetsuo Ueda
Feb 14, 2012·IEEE Transactions on Nanobioscience·Andrew Adamatzky
Jul 21, 2015·Journal of Molecular Biology·Madeleine Beekman, Tanya Latty
Nov 29, 2017·Computational Intelligence and Neuroscience·Xiaohua NieHaoyao Nie
Feb 3, 2019·Chaos·Guillermo FuertesMaría Alejandra Peralta
Oct 30, 2019·Scientific Reports·Fernando Patino-RamirezAudrey Dussutour

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Software Mentioned

MO
WOA
HSMA
SCA
GA
CC
hfill
DE
GSA
AM

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