A synergy of an evolutionary algorithm with slime mould algorithm through series and parallel construction for improving global optimization and conventional design problem

Abstract

The imitating behaviour of the slime mould algorithm (SMA) supports the collective efficiency, whereas the evolutionary algorithm (EA) smoothens the global effectiveness through a crossover. This observation leads to the integration of the EA with SMA. Thus, in this paper, an attempt has been made to hybridize the said algorithms in a parallel and series manner. In parallel structure, EA and SMA are executed parallelly and solutions obtained from both are combined to obtain the best solution which is further saved and updated as to a global best solution. Whereas, in a series structure, the population is fed to the EA initially to obtain the best solution which is again fed to SMA to obtain the global best solution. Under such guidance, the global search ability and search efficiency of EA and SMA are enhanced. The proposed PSEASMA and SSEASMA are tested on 23 classical benchmark functions and ten CEC2019 functions. The Wilcoxon rank-sum test is also carried out to validate the efficacy of the proposed work. The proposed work has been compared with other renowned metaheuristic algorithms in terms of average and standard deviation. Based on average and standard deviation, the ranks of each algorithm have been assigned through the Friedman test. The results suggested that the proposed method outperformed the other state of arts. The PSEASMA and SSEASMA have also been applied to classical engineering design problems. The performance of the proposed work is significantly superior compared to basic SMA, EA and other meta-heuristic algorithms.