: Establishing the foundation, including Optimization Models (classical vs. complexity theory), Representation (linear/nonlinear encoding), and Constraint Handling (reject, penalize, or repair strategies).
The content is generally divided into two main parts that categorize metaheuristics based on how they navigate the search space: Metaheuristics: From Design to Implementation (...
: Focuses on maintaining a set of solutions to explore the search space, including Genetic Algorithms , Swarm Intelligence (e.g., Ant Colonies, Particle Swarms), and Evolutionary Computation . Key Design & Implementation Stages : Establishing the foundation
: Focuses on local search-based methods that transform a single candidate solution, such as Simulated Annealing , Tabu Search , and Iterated Local Search . Representation (linear/nonlinear encoding)