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Guided local search is a metaheuristic search method. A meta-heuristic method is a method that sits on top of a local search algorithm to change its behavior. Guided local search builds up penalties during a search. It uses penalties to help local search algorithms escape from local minima and plateaus.
Local search is an anytime algorithm; it can return a valid solution even if it's interrupted at any time after finding the first valid solution. Local search is typically an approximation or incomplete algorithm because the search may stop even if the current best solution found is not optimal. This can happen even if termination happens ...
The aim of local search is that of finding an assignment of minimal cost, which is a solution if any exists. Point A is not a solution, but no local move from there decreases cost. However, a solution exists at point B. Two classes of local search algorithms exist. The first one is that of greedy or non-randomized algorithms. These algorithms ...
Local Search: LS 1997 Variable neighborhood search: VNS Trajectory-based - 1997 [7] Guided Local Search GLS Trajectory-based - 1998 [8] Clonal Selection Algorithm CSA Evolutionary-based - 2000 [9] Harmony Search: HS Evolutionary-based - 2001 [10] Memetic Algorithm: MA Evolutionary-based - 2002 Iterative Local Search ILS Trajectory-based - 2003 ...
A well known local search algorithm is the hill climbing method which is used to find local optimums. However, hill climbing does not guarantee finding global optimum solutions. Many metaheuristic ideas were proposed to improve local search heuristic in order to find better solutions. Such metaheuristics include simulated annealing, tabu search ...
Iterated local search kicks a solution out from a local optimum. Iterated Local Search [1] [2] (ILS) is a term in applied mathematics and computer science defining a modification of local search or hill climbing methods for solving discrete optimization problems. Local search methods can get stuck in a local minimum, where no improving ...
That means, the planning is done in two steps which is a guided local search in the original space. The advantage is that the number of nodes is smaller and the algorithm performs very well. The disadvantage is that a hierarchical pathplanner is difficult to implement. [10]
2-opt. In optimization, 2-opt is a simple local search algorithm for solving the traveling salesman problem.The 2-opt algorithm was first proposed by Croes in 1958, [1] although the basic move had already been suggested by Flood. [2]