A Novel Nature-Inspired Technique Based on Mushroom Reproduction for Constraint Solving and Optimization

摘要

Constraint optimization consists of looking for an optimal solution maximizing a given objective function while meeting a set of constraints. In this study, we propose a new algorithm based on mushroom reproduction for solving constraint optimization problems. Our algorithm, that we call Mushroom Reproduction Optimization (MRO), is inspired by the natural reproduction and growth mechanisms of mushrooms. This process includes the discovery of rich areas with good living conditions allowing spores to grow and develop their own colonies. Given that constraint optimization problems often suffer from a high-time computation cost, we thoroughly assess MRO performance on well-known constrained engineering and real-world problems. The experimental results confirm the high performance of MRO, comparing to other known meta-heursitcs, in dealing with complex optimization problems.