An evolutionary algorithm based hyper-heuristic framework for the set packing problem

Abstract

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights• An evolutionary algorithm based hyper-heuristic is proposed for the set packing problem (SPP) and the minimum weight dominating set (MWDS) problem.• Self-learning concept is employed in hyper-heuristic for a selection of heuristic.• Dynamic selection of parameter is adopted to make the approach, as much as possible, less dependent on parameter values. The empirical study shows the effectiveness of Dynamic selection of parameter.• The proposed approach has been compared with the respective state-of-the-art approaches for both the SPP and MWDS problem.• Computational results show the superiority of the proposed approach.

Solving the Set Packing Problem via a Maximum Weighted Independent Set Heuristic

“…Chaurasia et al presented an evolutionary algorithm-based hyperheuristic framework for solving the set packing problem [34]. Chaurasia and Kim proposed an evolutionary algorithm-based hyperheuristic framework that incorporates dynamic selection of parameters [35]. In [36], a decomposition technique based on constraint partitioning was proposed for exploiting the semiblock-angular structures of set packing problem and solving the original problem through solving the subproblems of the obtained structure.…”

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

“…A good high-level heuristic design requires that an appropriate LLH is selected at any particular point according to the current state of the solution, and a good design of acceptance criteria guides the search process toward an optimistic region [27] .…”