Multimodal Optimization

Many optimization problems are characterized by the existence of multiple local and, sometimes, global optima. Since most problems formulation for real world problems are not capable of capturing all of the constraints, these multiple solutions may be helpful in these situations enabling to choose a from a set of equally fit solutions located at different regions from the search space. This project will explore the idea of maximizing the diversity of a population of solutions in order to find multiple optima.

Related publications:

• Maximization of a dissimilarity measure for multimodal optimization.
• Maximizing Diversity for Multimodal Optimization.
• An artificial immune network for multimodal function optimization on dynamic environments.
• On the diversity mechanisms of opt-aiNet: A comparative study with fitness sharing.
• A concentration-based artificial immune network for combinatorial optimization.
• Conceptual and practical aspects of the aiNet family of algorithms.