  The advancement of industrialization has spurred the development of
innovative swarm intelligence algorithms, with Lion Swarm Optimization (LSO)
notable for its robustness, parallelism, simplicity, and efficiency. While LSO
excels in single-objective optimization, its multi-objective variants face
challenges such as poor initialization, local optima entrapment, and so on.
This study proposes Dynamic Multi-Objective Lion Swarm Optimization with
Multi-strategy Fusion (MF-DMOLSO) to address these limitations. MF-DMOLSO
comprises three key components: initialization, swarm position update, and
external archive update. The initialization unit employs chaotic mapping for
uniform population distribution. The position update unit enhances behavior
patterns and step size formulas for cub lions, incorporating crowding degree
sorting, Pareto non-dominated sorting, and Levy flight to improve convergence
speed and global search capabilities. Reference points guide convergence in
higher-dimensional spaces, maintaining population diversity. An adaptive
cold-hot start strategy generates a population responsive to environmental
changes. The external archive update unit re-evaluates solutions based on
non-domination and diversity to form the new population. Evaluations on
benchmark functions showed MF-DMOLSO surpassed multi-objective particle swarm
optimization, non-dominated sorting genetic algorithm II, and multi-objective
lion swarm optimization, exceeding 90% accuracy for two-objective and 97% for
three-objective problems. Compared to non-dominated sorting genetic algorithm
III, MF-DMOLSO showed a 60% improvement. Applied to 6R robot trajectory
planning, MF-DMOLSO optimized running time and maximum acceleration to 8.3s and
0.3pi rad/s^2, achieving a set coverage rate of 70.97% compared to 2% by
multi-objective particle swarm optimization, thus improving efficiency and
reducing mechanical dither.
