Multi-Objective Optimization Based on Artificial Intelligence and Evolutionary Algorithms
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 14 June 2026 | Viewed by 155
Special Issue Editor
Special Issue Information
Dear Colleagues,
Complex real-world challenges increasingly demand the integration of artificial intelligence (AI) and evolutionary algorithms (EAs) to address multi-objective optimization problems. These approaches synergize computational intelligence (including population-based heuristics), data-driven learning, and multi-criteria decision-making techniques to navigate high-dimensional, dynamic, and uncertain design spaces. The rapid evolution of AI-enhanced EAs—evidenced by surging publications and open-source tools—has reshaped fields from sustainable engineering to fintech. To harness this momentum, we invite contributions bridging AI, EAs, and decision-making to pioneer next-generation optimization frameworks.
This Special Issue aims to unite researchers exploring AI–EA fusion for multi-objective optimization. We seek to foster cross-disciplinary dialogue between AI, evolutionary computation, and decision-making communities; accelerate the translation of theoretical advances into practical solutions; and highlight emerging applications requiring AI–EA hybridization.
We welcome original research articles, reviews, and case studies on the following topics:
- AI-Driven Optimization: Neural networks, reinforcement learning, transformers, and deep learning for surrogate modeling or adaptive operators;
- Evolutionary and Swarm Intelligence: Novel EA variants (e.g., NSGA-III, MOEA/D), particle swarm, ant colony, or co-evolutionary algorithms;
- Hybrid AI-EA Frameworks: Integration of machine learning with EAs for constraint handling, convergence acceleration, or preference articulation;
- Large-Scale and Many-Objective Optimization: Scalable algorithms for >3 objectives, dimensionality reduction, and explainable Pareto fronts;
- Constrained Multi-Objective Optimization: Novel constraint handling techniques for multi-objective optimization problems with constraints;
- Uncertainty-Aware Optimization: Robustness under noise, fuzzy logic, Bayesian optimization, and stochastic environments;
- Real-World Applications: Sustainable systems, Industrial 4.0, biomedicine, smart cities, climate modeling, financial portfolio optimization, and risk management.
Dr. Zhizhong Liu
Guest Editor
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Keywords
- multi-objective evolutionary algorithms (MOEAs)
- swarm intelligence
- explainable AI in decision-making
- constrained multi-objective optimization
- high-dimensional Pareto fronts
- data-driven surrogate modeling
- robust optimization under uncertainty
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