Work

ENSGA-II: Efficient non-dominated sorting genetic algorithm framework with finite-difference diversity for router placement optimization

Year: 2026

Type: article

Abstract: This study proposes an efficient non-dominated sorting genetic algorithm II ( ENSGA-II ) for router placement optimization in industrial wireless mesh networks. Unlike conventional NSGA-II , which relies on a first-order crowding distance, ENSGA-II incorporates a finite-difference–based diversity measure to better reflect the geometric characteristics of the Pareto front. Evaluation using Hypervolume (HV), Inverted Generational Distance (IGD), and IGD + shows that ENSGA-II achieves more accurate and stable Pareto-front approximations than NSGA-II , SPEA2 , MOEA/D , and HHO .

Source: ICT Express

DOI: 10.1016/j.icte.2026.03.019

Authors: Bitchan Eom, Eunchan Kim

Institution: Hanyang University

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500