Mathematical Foundations of Multiobjective Optimization and Evolutionary Computation

Dear Colleagues,

Multiobjective optimization and evolutionary computation have become indispensable tools for solving complex real-world problems involving conflicting objectives, nonlinear constraints, uncertainty, and high dimensionality. Despite significant algorithmic advances, a rigorous understanding of their mathematical foundations remains critical for improving performance, interpretability, robustness, and generalization across domains.

This Special Issue aims to provide a comprehensive platform for advancing the theoretical and mathematical underpinnings of multiobjective optimization and evolutionary computation. We invite contributions that address convergence properties, optimality conditions, landscape analysis, diversity preservation, preference modeling, and complexity analysis of single- and multiobjective evolutionary algorithms. Emphasis is placed on formal analyses that bridge theory and practice, including stability, scalability, explainability, and robustness under dynamic, noisy, or constrained environments.

The Special Issue also welcomes studies that integrate mathematical modeling with emerging paradigms such as surrogate-assisted optimization, counterfactual and causal learning, dynamic and many-objective optimization, and hybridization with machine learning techniques. Application-driven papers are encouraged, provided they offer strong theoretical insights or novel mathematical formulations.

By bringing together researchers from optimization theory, evolutionary computation, and applied mathematics, this Special Issue seeks to strengthen the theoretical foundations of evolutionary multiobjective optimization and inspire the next generation of principled, efficient, and reliable optimization methodologies.

Dr. Rammohan Mallipeddi
Guest Editor

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