Dynamic Multi-Objective Lion Swarm Optimization with Multi-strategy Fusion: An application in 6R robot trajectory planning

TL;DR

This paper introduces MF-DMOLSO, a novel multi-strategy fusion-based multi-objective lion swarm optimization algorithm, which significantly improves convergence, diversity, and efficiency in complex optimization tasks like robot trajectory planning.

Contribution

The study proposes MF-DMOLSO with innovative components such as chaotic initialization, enhanced update strategies, and adaptive cold-hot start, addressing limitations of existing multi-objective lion swarm algorithms.

Findings

MF-DMOLSO outperforms existing algorithms with over 90% accuracy on benchmark functions.

It shows a 60% improvement over NSGA-III in multi-objective optimization.

Applied to 6R robot trajectory planning, it reduces running time and maximum acceleration, improving efficiency.

Abstract

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.