Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems

This paper aims to represent a multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA) to solve real-world constraint engineering problems. The proposed algorithm has a better optimization performance than the existing multi-objective slime mould algorithm. In the MOEOSMA, dynamic coefficients are used to adjust exploration and exploitation trends. The elite archiving mechanism is used to promote the convergence of the algorithm. The crowding distance method is used to maintain the distribution of the Pareto front. The equilibrium pool strategy is used to simulate the cooperative foraging behavior of the slime mould, which helps to enhance the exploration ability of the algorithm. The performance of MOEOSMA is evaluated on the latest CEC2020 functions, eight real-world multi-objective constraint engineering problems, and four large-scale truss structure optimization problems. The experimental results show that the proposed MOEOSMA not only finds more Pareto optimal solutions, but also maintains a good distribution in the decision space and objective space. Statistical results show that MOEOSMA has a strong competitive advantage in terms of convergence, diversity, uniformity, and extensiveness, and its comprehensive performance is significantly better than other comparable algorithms.