Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Enhanced Hyper-Cube Framework Aco for Combinatorial Optimization Problems

Version 1 : Received: 30 August 2021 / Approved: 31 August 2021 / Online: 31 August 2021 (11:55:51 CEST)

How to cite: Ahmid, A.; Dao, T.; Le, N.V. Enhanced Hyper-Cube Framework Aco for Combinatorial Optimization Problems. Preprints 2021, 2021080573 (doi: 10.20944/preprints202108.0573.v1). Ahmid, A.; Dao, T.; Le, N.V. Enhanced Hyper-Cube Framework Aco for Combinatorial Optimization Problems. Preprints 2021, 2021080573 (doi: 10.20944/preprints202108.0573.v1).

Abstract

Many combinatorial optimization problems are hard to solve within the polynomial computational time or NP-hard problems. Therefore, developing new optimization techniques or improving existing ones still grab attention. This paper presents an improved variant of the Ant Colony Optimization meta-heuristic called Enhanced Hyper Cube Framework ACO (EHCFACO). This variant has an enhanced exploitation feature that works through two added local search movements of insertion and bit flip. In order to examine the performance of the improved meta-heuristic, a well-known structural optimization problem of laminate Stacking Sequence Design (SSD) for maximizing critical buckling load has been used. Furthermore, five different ACO variants were concisely presented and implemented to solve the same optimization problem. The performance assessment results reveal that EHCFACO outperforms the other ACO variants and produces a cost-effective solution with considerable quality.

Keywords

Combinatorial optimization; Ant Colony Optimization (ACO); Buckling load factor; Composite laminate.

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