Title: MaOEA/APP-PBI: a many-objective evolutionary algorithm based on adaptive projection plane and PBI function

Authors: Weiwei Yu; Jing Wang; Yanxiang Deng

Addresses: School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China ' School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China ' School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

Abstract: Many-objective optimisation challenges traditional Pareto-based evolutionary algorithms. This work introduces a self-adjusting dominance relation using an adaptive projection plane, enhancing convergence by leveraging distances to the plane and between projection points without extra parameters. It also proposes a PBI-function-based method to balance convergence and diversity in solution screening. These techniques are integrated into the NSGA-II framework, creating the MaOEA/APP-PBI algorithm. Evaluated on 5-, 10-, 15-, and 20-objective DTLZ and WFG problems using IGD and HV metrics, MaOEA/APP-PBI outperforms six leading algorithms. Results demonstrate its significantly superior convergence and diversity across various objectives, highlighting its effectiveness for many-objective optimisation.

Keywords: many-objective optimisation problem; many-objective evolutionary algorithm; dominant relationship; convergence; diversity.

DOI: 10.1504/IJBIC.2026.152565

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.2, pp.73 - 102

Received: 25 Dec 2024
Accepted: 06 Jun 2025
Published online: 27 Mar 2026 *

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