EMO - Evolutionary Multiobjective Optimization

Description

In many real-world applications, multiple objective functions must be optimized simultaneously, leading to a multiobjective optimization problem (MOP), for which a single ideal solution seldomly exists. Instead, MOPs typically admit multiple compromise solutions representing different trade-offs among the objectives. Due to their applicability to a wide range of MOPs, including black-box problems, evolutionary and other randomized optimization algorithms for multiobjective optimization have given rise to an important and active research area known as Evolutionary Multiobjective Optimization (EMO). EMO algorithms do not require continuity or differentiability assumptions and can handle problem characteristics such as nonlinearity, multimodality, and stochasticity. Furthermore, preference information from a decision-maker can be used to deliver a finite-size approximation to the optimal solution set (the Pareto-optimal set) in a single optimization run.

Scope

The Evolutionary Multiobjective Optimization (EMO) Track brings together researchers from this field and related areas to explore all facets of EMO development and application. The track covers a wide range of topics, including but not limited to:

• Handling continuous, combinatorial, or mixed-integer MOPs
• Benchmarking methodologies, including test problems and performance assessment
• Benchmarking studies, especially in comparison to non-EMO approaches
• Selection and variation mechanisms
• Hybridization techniques, parallel and distributed models
• Software development and implementation aspects
• Convergence assessment and stopping criteria
• Theoretical foundations and search space analysis that bring new insights into EMO
• Visualization techniques for solution sets and multiobjective landscapes
• EMO algorithm selection and configuration
• Preference articulation and interactive EMO
• Many-objective optimization, large-scale optimization
• Computationally expensive objectives
• Constraint handling, uncertainty handling
• Real-world applications that go beyond solving specific problems, bringing new and broader insights into EMO