Lamarckian Evolution Based Algorithm for Multi-robot Path Planning Problem

Abstract

This study addresses the issue of decreasing computational resources spent on solving the multi-robot path planning (MRPP) problem. We propose a novel algorithm inspired by Lamarckian evolution principles integrated into the traditional Darwin evolutionary algorithm. The proposed Lamarckian Evolution-Based (LEB) algorithm aims to enhance both the convergence rate and the accuracy of the MRPP problem solution, thereby reducing overall computational effort. Through extensive simulations, the LEB algorithm demonstrated superior performance compared to standard approaches such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Despite requiring additional computational resources per iteration due to added local optimization stages, the LEB algorithm achieves significant reductions in total resource expenditure and improves solution quality within a specified computational budget. Our findings highlight the potential of Lamarckian-inspired strategies for multi-robot navigation tasks efficiency.