Abstract

Multi-objective Optimization algorithms like Swarm Intelligence Algorithms are becoming more useful in complex, dynamic collaborative robot working environments. These environments allow humans and robots to work securely and productively. Collaborative robot behaviour is aimed to be optimised via swarm intelligence techniques. Social insect-inspired swarm intelligence algorithms are robust and adaptive to multi-objective Optimization approaches. They enjoy exploring the solution space and balance the competing goals. These algorithms let collaborative robots balance task productivity, safety, human-robot interaction, energy utilization, and other related issues. This paper is aimed to build swarm intelligence algorithms for multi-objective Optimization in a collaborative robot environment. Define objectives, formulate the problem, choose the algorithm, encode and decode robot configurations, fitness assignment, initialization, iterative Optimization, convergence, and post-processing discoveries. Swarm intelligence algorithms help collaborative robots navigate their workplace, adapt, and safely interact with humans. Decision-makers can choose Pareto front or non-dominated solutions for optimal trade-offs. Validation and simulation enable Pareto-optimal and safe and collaborative robot behaviours. Fine-tuning algorithm parameters enhance performance and convergence. Collective intelligence solves complex, dynamic multi-objective Optimization issues. As collaborative robots transform many industries, swarm intelligence algorithms will shape safe, efficient, and productive human-robot interactions.

Keywords:Swarm Intelligence; Multi-objective Optimization approaches; Collaborative robots; Multiobjective Optimization; Pareto-optimal; Particle Swarm Optimization