An Elitist-Based Multi-Objective Moth-Flame Algorithm for Engineering Design Problem

The utilization of meta-heuristic algorithms for addressing complex engineering problems is widely recognized as an effective approach. However, it's still challenging to obtain diverse solutions to a problem through meta-heuristic algorithms. A new multi-objective moth-flame optimization algorithm(EMMFO) is proposed in this paper. The EMMFO algorithm is a novel optimization approach that integrates the principles of the moth-flame optimization (MFO) algorithm and the elitist strategy. In the flame environmental selection stage, EMMFO employs a non-dominated sorting method to maintain an elite set of solutions. Additionally, the EMMFO algorithm incorporates the Levy flight mechanism to enhance global exploration capability. To evaluate the performance, DTLZ and ZDT test function is employed to test the performance of EMMFO. Meanwhile EMMFO is applied to the gear train design problem and compared with several multi-objective optimization algorithms. Experimental results demonstrate that EMMFO exhibits superior performance and robustness in solving the gear train design problem.