Research Area:  Metaheuristic Computing

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 cha- otic 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.

Keywords:  
Slime mold algorithm (SMA)
CSMA
Convergence rate
Metaheuristic algorithm
Engineering design
Optimization

Author(s) Name:  Dinesh Dhawale, Vikram Kumar Kamboj, Priyanka Anand

Publisher name:  Springer

DOI:  10.1007/s00366-021-01409-4

Volume Information:  38, pages 2739–2777 (2022)

Paper Link:   https://link.springer.com/article/10.1007/s00366-021-01409-4