Mushroom Picking Framework with Cache Memories for Solving Job Shop Scheduling Problem

Abstract

Applying population-based metaheuristics is a known method of solving difficult optimization problems. In this paper the search for the best solution is conducted by decentralized, self-organized agents, working in parallel threads, in the so called mushroom-picking method. The search is enhanced by remembering in which part of the recently improved solution the last successful change took place and intensifying the search in this part. A computational experiment shows that introducing the component for remembering the most recent changes may improve the results obtained by the model in the case of JSSP problems.