Single Solution Based Metaheuristics
Frédéric Héliodore, Amir Nakib, Boussaad Ismail, Salma Ouchraa, Laurent Schmitt · 2017
The objective of optimization is to find the global and/or the local optimum or optima. Depending on the optimization problem being addressed, one or more methods can be applied, and one of them may be more suitable than the others. These methods include the class of path-based methods also called single-solution metaheuristics. This chapter introduces a number of path-based methods starting with most common algorithms, notably descent methods, simulated annealing, microcanonical annealing and even tabu search. It proceeds with exploratory local search algorithms that incorporate other path-based methods such as the Greedy Randomized Adaptive Search (GRASP) method, variable neighborhood search, guided local search and iterated local search. The chapter also presents other methods such as Nelder and Mead's simplex method, the noising method and smoothing methods. All these methods are considered to be local searches, because they are based on intensification that allows a good quality solution to be obtained.