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 Topics of interest include, but are not limited to:


  • Techniques such as tabu search, simulated annealing, iterated local search, variable neighborhood search, bio-inspired algorithms, memory-based optimization, evolutionary algorithms, memetic algorithms, ant colony optimization, particle swarm optimization, scatter search, path relinking, hybrid metaheuristics, simheuristics, matheuristics, etc. Including techniques that enhance the usability and increase the potential of metaheuristic algorithms such as parallelization of algorithms, reactive search mechanisms for self-tuning, offline metaheuristic algorithm configuration techniques, algorithm portfolios, etc.


  • Empirical and Theoretical Research in metaheuristics including large-scale experimental analyses, algorithm comparisons, new experimental methodologies, engineering methodologies for stochastic local search algorithms, search space analysis, theoretical insights into properties of metaheuristic algorithms. Including applications of well-known and classical problems as Traveling Salesman Problem, Vehicle Routing Problems, Scheduling Problems, Location Problems, etc.


  • Industrial Applications of metaheuristics in fields such as transportation, health care, bioinformatics, data mining, planning and scheduling, production and operations management, economics, marketing, telecommunications, logistics and supply chains, etc. Particularly, it is welcomed are innovative applications of metaheuristics with high impact in real business and organization.


  • Contributions on the Interface of Metaheuristics with other disciplines, such as agent-based models, integer programming, constraint programming, machine learning, deep learning, etc.


  • Challenging New Problems such as big data and large-scale optimization problems, multi-objective, stochastic, dynamic problems and new challenge problems.