Slime Mould Metaheuristic for optimization and robot path planning

Abstract

Function optimization represents a remarkable challenge in industry and society, aiming to find reasonable solutions –even if they are suboptimal– for everyday problems. Metaheuristics drive the optimization search towards the goals using a specific algorithm inspired by different concepts: from industrial processes to the behaviour of living beings in nature, from mathematical ideas to physics notions. This research proposes a new metaheuristic inspired by the Slime Mould and its foraging behaviours. On the one hand, an exploitation stage mimics the greedy amoeba’s conduct when food is plenty. On the other hand, an exploration stage copies the fruity aggregation of the cells and the subsequent spore dissemination. This study compares the most cited metaheuristics and the Slime Mould Optimization in two different experimentation stages: on the one hand, the optimization of standard benchmarking functions; on the other hand, solving the robot path planning problem. Moreover, a hybridization of the SMO and the WOA is presented, which keeps the SMO’s convergence speed and the WOA’s good performance in finding the best solutions. • Proposes a new nature-inspired metaheuristic for optimization based on the behaviour of the Slime Mould. • This metaheuristic is compared with other well-known alternatives on a benchmark of standard mathematical functions. • The metaheuristics are also compared on the robot path planning problem. • The Slime Mould Optimization (SMO) algorithm shows high convergence speed with a reasonable performance. • In the robot path planning problem, SMO shows the best path in all the scenarios without incurring in excessive computational costs.