Скачать или смотреть Session 2 – Local Search, Simulated Annealing, Tabu Search & VND | LSO 2025 Workshop

This video is the second session of the online workshop “An Introduction to Metaheuristics”, conducted on 13 December 2025 (4:00–6:00 PM) as part of the Large Scale Optimization Workshop (LSO 2025).

The workshop is hosted by the Brij Disa Centre for Data Science and Artificial Intelligence, Indian Institute of Management Ahmedabad (IIM Ahmedabad). It is designed for students, faculty members, and industry professionals, and focuses on building a strong conceptual foundation for designing and implementing metaheuristic algorithms.

Across the three-day workshop, widely used metaheuristic techniques such as Local Search, Simulated Annealing, Tabu Search, and Genetic Algorithms are discussed, with an emphasis on conceptual understanding rather than implementation details.

Workshop slides and resources:
https://sites.google.com/view/shubham...

This Session (Session 2) focuses on single-solution based metaheuristics, which iteratively improve a single candidate solution using neighborhood-based search strategies. The session builds on the concepts introduced in Session 1 and explains how different mechanisms are used to escape local optima and explore the search space effectively.

Topics covered in this session include:

• Local Search and hill-climbing strategies
• Neighborhood structures and move evaluation
• Simulated Annealing: acceptance of non-improving solutions, temperature, and cooling schedules
• Tabu Search: tabu list, aspiration criteria, intensification, and diversification
• Variable Neighborhood Descent (VND) and the systematic use of multiple neighborhoods

This session provides a detailed conceptual understanding of deterministic and stochastic local search methods and prepares the foundation for population-based metaheuristics discussed in the next session.

Instructor:
Shubham Keshri
Ph.D. Scholar
Department of Management Sciences
Indian Institute of Technology Kanpur

Reference:
Talbi, E. G., Metaheuristics: From Design to Implementation, John Wiley & Sons, 2009.