Local search (optimization)

Examples

Some problems where local search has been applied are:

1. The vertex cover problem, in which a solution is a vertex cover of a graph, and the target is to find a solution with a minimal number of nodes
2. The travelling salesman problem, in which a solution is a cycle containing all nodes of the graph and the target is to minimize the total length of the cycle
3. The boolean satisfiability problem, in which a candidate solution is a truth assignment, and the target is to maximize the number of clauses satisfied by the assignment; in this case, the final solution is of use only if it satisfies all clauses
4. The nurse scheduling problem where a solution is an assignment of nurses to shifts which satisfies all established constraints
5. The k-medoid clustering problem and other related facility location problems for which local search offers the best known approximation ratios from a worst-case perspective
6. The Hopfield Neural Networks problem for which finding stable configurations in Hopfield network.

Description

Most problems can be formulated in terms of search space and target in several different manners. For example, for the travelling salesman problem a solution can be a cycle and the criterion to maximize is a combination of the number of nodes and the length of the cycle. But a solution can also be a path, and being a cycle is part of the target.

A local search algorithm starts from a candidate solution and then iteratively moves to a neighbor solution. This is only possible if a neighborhood relation is defined on the search space. As an example, the neighborhood of a vertex cover is another vertex cover only differing by one node. For boolean satisfiability, the neighbors of a truth assignment are usually the truth assignments only differing from it by the evaluation of a variable. The same problem may have multiple different neighborhoods defined on it; local optimization with neighborhoods that involve changing up to k components of the solution is often referred to as k-opt.

Typically, every candidate solution has more than one neighbor solution; the choice of which one to move to is taken using only information about the solutions in the neighborhood of the current one, hence the name local search. When the choice of the neighbor solution is done by taking the one locally maximizing the criterion, the metaheuristic takes the name hill climbing. When no improving configurations are present in the neighborhood, local search is stuck at a locally optimal point. This local-optima problem can be cured by using restarts (repeated local search with different initial conditions), or more complex schemes based on iterations, like iterated local search, on memory, like reactive search optimization, on memory-less stochastic modifications, like simulated annealing.

Termination of local search can be based on a time bound. Another common choice is to terminate when the best solution found by the algorithm has not been improved in a given number of steps. Local search is an anytime algorithm: it can return a valid solution even if it's interrupted at any time before it ends. Local search algorithms are typically approximation or incomplete algorithms, as the search may stop even if the best solution found by the algorithm is not optimal. This can happen even if termination is due to the impossibility of improving the solution, as the optimal solution can lie far from the neighborhood of the solutions crossed by the algorithms.

For specific problems it is possible to devise neighborhoods which are very large, possibly exponentially sized. If the best solution within the neighborhood can be found efficiently, such algorithms are referred to as very large-scale neighborhood search algorithms.

See also

Local search is a sub-field of:

• Metaheuristics
• Stochastic optimization
• Optimization

Fields within local search include:

• Hill climbing
• Simulated annealing (suited for either local or global search)
• Tabu search
• Late acceptance hill climbing
• Reactive search optimization (combining machine learning and local search heuristics)

Real-valued search-spaces

Several methods exist for performing local search of real-valued search-spaces:

• Luus–Jaakola searches locally using a uniform distribution and an exponentially decreasing search-range.
• Random optimization searches locally using a normal distribution.
• Random search searches locally by sampling a hypersphere surrounding the current position.
• Pattern search takes steps along the axes of the search-space using exponentially decreasing step sizes.

References

Bibliography

• Battiti, Roberto; Mauro Brunato; Franco Mascia (2008). Reactive Search and Intelligent Optimization. Springer Verlag. ISBN 978-0-387-09623-0. Archived from the original on 2012-03-16.
• Hoos, H.H. and Stutzle, T. (2005) Stochastic Local Search: Foundations and Applications, Morgan Kaufmann.
• Vijay Arya and Naveen Garg and Rohit Khandekar and Adam Meyerson and Kamesh Munagala and Vinayaka Pandit, (2004): Local Search Heuristics for k-Median and Facility Location Problems, SIAM Journal of Computing 33(3).
• Juraj Hromkovič: Algorithmics for Hard Problems: Introduction to Combinatorial Optimization, Randomization, Approximation, and Heuristics (Springer)
• Wil Michiels, Emile Aarts, Jan Korst: Theoretical Aspects of Local Search (Springer)