Multiobjective Optimization: Theory, Methods and Applications

Dear Colleagues,

Optimization is an indispensable tool in engineering, driving improvements in the performance, efficiency, cost-effectiveness, and sustainability of various systems in different disciplines. Multi-objective optimization (MOO) is frequently used for finding optimal solutions to complex problems in engineering domains when multiple objectives, especially efficiency and effectiveness maximization, are taken into account. Through the optimization of related objective functions, decision-makers are able to consider various trade-offs and make well-informed decisions that optimally align with their preferences. Multi-objective optimization is becoming an increasingly significant tool for tackling real-world problems following the algorithmic intelligence embodied in various models.

Though MOO has many applications and is highly significant, there are a number of theoretical, methodological, and practical issues associated with it. For example, it is difficult to handle stochasticity and variability in problem parameters, find Pareto-optimal solutions efficiently while balancing convergence and diversity, and lower computational costs without sacrificing the solutions’ optimality. Addressing these issues and enhancing MOO’s practical utility will require advancements in evolutionary algorithms, machine learning-assisted optimization, theoretical research, and hybrid approaches that integrate many techniques to ensure the best performance. Research is still being conducted on how to create advanced algorithms that can manage stochastic and uncertain issues in environments, as well as deal with the challenges related to large-scale, many-objective, constrained, and computation-expensive optimization problems. Hence, this Special Issue is expected to explore a wide range of topics related to multi-objective optimization, including the theoretical explanation and demonstration of MOO, advanced methods and AI technologies for MOO, and the application verification of MOO in real industrial scenarios.

Scope and Topics

The topics of this Special Issue include, but are not limited to, the following:

• Novel algorithms (e.g., mathematical programming methods, reinforcement learning methods, meta-learning, transfer learning, deep learning methods, and other optimizers) for solving multiobjective optimization problems;
• Algorithms for solving special multiobjective optimization problems, e.g., sparse problems, constrained problems, expensive problems, dynamic problems, multimodal problems, multitask problems, and large-scale problems;
• Algorithms for solving combinatorial multiobjective optimization problems, e.g., subset selection, vehicle routing, recommendation, scheduling, networking, blockchain, and hybrid encoding problems.;
• Algorithms for solving real-world multiobjective optimization problems, e.g., machine learning, data mining, manufacturing, scheduling, electronics, economics, aeronautical and aerospace, communication, and control;
• Special methods for handling conflicting objectives, e.g., dominance, gradient guidance, and generative models;
• Performance evaluation, theoretical analysis, visualization and interpretation, and benchmarks of multiobjective optimization problems.

Prof. Dr. Mengchu Zhou
Prof. Dr. Qi Kang
Guest Editors

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• intelligent optimization
• multiobjective optimization
• engineering optimization problems
• expensive optimization problems
• machine leaning