IBMSMA: An Indicator-based Multi-swarm Slime Mould Algorithm for Multi-objective Truss Optimization Problems

This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity; the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution; and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.

Automated summary

This study proposes an improved multi-objective slime mould algorithm (IBMSMA) for solving the multi-objective truss optimization problem. IBMSMA utilizes chaotic grouping mechanism, dynamic regrouping strategy, shift density estimation, and Pareto external archive to improve diversity, select superior search agents, and maintain convergence and distribution of non-dominated solutions. The performance of IBMSMA is evaluated by applying it to eight multi-objective truss optimization problems and comparing the results with 14 other optimization algorithms using hypervolume, inverted generational distance, and spacing-to-extent indicators. The results show that IBMSMA outperforms state-of-the-art algorithms in terms of convergence, diversity, and efficiency for large-scale engineering design problems.