Optimization problem involving interval parameters and multiple conflicting objectives are called multi-objective optimization problems with interval parameters (IMOPs), which are common and hard to be solved effectively in practical applications. An interval multi-objective honey badger algorithm (IMOHBA) is proposed to address the IMOPs in this paper. Firstly, the \(\mu\) metric is employed to assess the Pareto dominance relationship among interval individuals, which reflects the quality of the optimal solutions. Secondly, the crowding distance suitable for the interval objective is utilized to reflect the distribution of the optimal solution. Finally, the candidate solutions are ranked and selected by the non-dominated sorting method. To validate the performance of IMOHBA, it is tested on 19 benchmark IMOPs as well as an interval multi-objective scheduling problem for underwater wireless sensor networks and compared with three state-of-the-art algorithms. The experimental results demonstrate the superiority and strong competitiveness of IMOHBA in addressing IMOPs, exhibiting improved convergence and broader exploration capabilities of the solution space. These findings further validate the effectiveness and feasibility of IMOHBA, highlighting its unique advantage in solving IMOPs.
