Balancing Pareto Front exploration of Non-dominated Tournament Genetic Algorithm (B-NTGA) in solving multi-objective NP-hard problems with constraints

TL;DR

This paper introduces B-NTGA, a balanced genetic algorithm that improves Pareto Front exploration in multi-objective NP-hard problems with constraints, demonstrating superior efficiency and performance over existing methods.

Contribution

The paper proposes a novel balanced selection operator for B-NTGA that enhances Pareto Front exploration and adaptivity in constrained multi-objective optimization.

Findings

B-NTGA outperforms state-of-the-art methods in benchmark problems.

The balancing mechanism reduces over-sampling of certain Pareto regions.

Experimental results confirm higher efficiency and better solution quality.

Abstract

The paper presents a new balanced selection operator applied to the proposed Balanced Non-dominated Tournament Genetic Algorithm (B-NTGA) that actively uses archive to solve multi- and many-objective NP-hard combinatorial optimization problems with constraints. The primary motivation is to make B-NTGA more efficient in exploring Pareto Front Approximation (PFa), focusing on 'gaps' and reducing some PFa regions' sampling too frequently. Such a balancing mechanism allows B-NTGA to be more adaptive and focus on less explored PFa regions. The proposed B-NTGA is investigated on two benchmark multi- and many-objective optimization real-world problems, like Thief Traveling Problem and Multi-Skill Resource-Constrained Project Scheduling Problem. The results of experiments show that B-NTGA has a higher efficiency and better performance than state-of-the-art methods.