MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem

Abstract

The resource constrained project scheduling problem (RCPSP) is an NP-Hard combinatorial optimization problem. The objective of RCPSP is to schedule a set of activities without violating any activity precedence or resource constraints. In recent years researchers have moved away from complex solution methodologies, such as meta heuristics and exact mathematical approaches, towards more simple intuitive solutions like priority rules. This often involves using a genetic programming based hyper-heuristic (GPHH) to discover new priority rules which can be applied to new unseen cases. A common problem affecting GPHH is diversity in evolution which often leads to poor quality output. In this paper, we present a MAP-Elites based hyper-heuristic (MEHH) for the automated discovery of efficient priority rules for RCPSP. MAP-Elites uses a quality diversity based approach which explicitly maintains an archive of diverse solutions characterised along multiple feature dimensions. In order to demonstrate the benefits of our proposed hyper-heuristic, we compare the overall performance against a traditional GPHH and priority rules proposed by human experts. Our results indicate strong improvements in both diversity and performance. In particular we see major improvements for larger instances which have been under-studied in the existing literature.

A Quantum-Inspired Predator–Prey Algorithm for Real-Parameter Optimization

“…Metaheuristic optimization algorithms [1] have gained attention as a promising alternative to traditional optimization methods such as linear programming, nonlinear programming, and dynamic programming [2,3]. Metaheuristic algorithms have the ability to explore large solution spaces efficiently and handle complex problems that are difficult to model mathematically or have a large number of variables and constraints [4]. These algorithms aim to solve optimization problems by mimicking the behavior of natural processes such as biological evolution [5,6].…”