An Improved Elite Slime Mould Algorithm for Engineering Design

The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, the structure of SMA is adjusted to develop the efficiency of the original method. As a stochastic optimizer, SMA mainly stimulates the behavior of slime mold in nature. For the harmony of the exploration and exploitation of SMA, the paper proposed an enhanced algorithm of SMA called ECSMA, in which two mechanisms are embedded into the structure: elite strategy, and chaotic stochastic strategy. The details of the original SMA and the two introduced strategies are given in this paper. Then, the advantages of the improved SMA through mechanism comparison, balance-diversity analysis, and contrasts with other counterparts are validated. The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA. Also, SMA is applied to four structural design issues of the welded beam design problem, PV design problem, I-beam design problem, and cantilever beam design problem with excellent results.

Automated summary

This paper focuses on the improvement and mitigation of the stagnation problems of Slime mould algorithm (SMA) through adjusting its structure and incorporating elite strategy and chaotic stochastic strategy. The experimental results validate the significant enhancing effect of both mechanisms on SMA and its excellent performance in four structural design issues.