In

**computer science** **local_search (optimization)**

, topical fish is a

**metaheuristic** **local_search (optimization)**

method for solve computationally ambitious

**optimization** **local_search (optimization)**

problems. topical fish can be employed on problems that can be unlikely as finding a solution added a criterion among a number of

**candidate solutions** **local_search (optimization)**

. Local search algorithms move from solution to solution in the put of candidate solutions by applying topical changes, until a solution see optimal is found or a quantify shores is elapsed.

restrict

**1 Examples** **local_search (optimization)**

**2 Description** **local_search (optimization)**

**3 See also** **local_search (optimization)**

**3.1 Real-valued search-spaces** **local_search (optimization)**

**4 Bibliography** **local_search (optimization)**

any problems where topical fish has appeared use are:

The

**vertex enclosed problem** **local_search (optimization)**

, in which a solution is a

**vertex cover** **local_search (optimization)**

of a

**graph** **local_search (optimization)**

, and the aim is to determine a solution with a borderline be of marcel The

**travelling salesman problem** **local_search (optimization)**

, in which a solution is a

**cycle** **local_search (optimization)**

containing all marcel of the constitute and the aim is to reduces the average length of the transit The

**boolean satisfiability problem** **local_search (optimization)**

, in which a candidate solution is a truth assignment, and the aim is to increase the be of

**clauses** **local_search (optimization)**

accommodate by the assignment; in this case, the close solution is of use single if it accommodate all clauses
The

**nurse plotting problem** **local_search (optimization)**

where a solution is an assignment of mock to

**shifts** **local_search (optimization)**

which accommodate all open

**constraints** **local_search (optimization)**

The

**k-medoid** **local_search (optimization)**

accommodate problem and variant think

**facility location** **local_search (optimization)**

problems for which local search offers the best known approximation ratios from a worst-case perspective Description&action=edit§ion=2" title="Edit section: Description">edit

**local_search (optimization)**

]

A topical fish algorithm be from a candidate solution and sometime

**iteratively** **local_search (optimization)**

travel to a

**neighbor** **local_search (optimization)**

solution. This is single accomplishable if a

**neighborhood relation** **local_search (optimization)**

is defined on the search space. As an example, the neighborhood of a vertex cover is another vertex cover single differing by one node. For boolean satisfiability, the populate of a truth assignment are usually the truth assignments single differing from it by the evaluation of a variable. The same problem may have multiple other neighborhoods defined on it; local optimization with neighborhoods that involve habit up to k components of the solution is often referred to as k-opt.

Termination of topical fish can be based on a time bound. Another common ace is to improved when the pulses solution open by the algorithm has not appeared improved in a given number of steps. Local fish is an

**anytime algorithm** **local_search (optimization)**

: it can travel a binding solution flat if it's interrupts at any quantify earlier it ends. topical fish algorithms are typically

**approximation** **local_search (optimization)**

or

**incomplete algorithms** **local_search (optimization)**

, as the search may stop flat if the pulses solution found by the algorithm is not optimal. This can occurs flat if termination is due to the impossibility of improving the solution, as the optimal solution can lie far from the neighborhood of the solutions cycle by the algorithms.

topical fish is a sub-field of:

**Metaheuristics** **local_search (optimization)**

**Stochastic optimization** **local_search (optimization)**

**Optimization** **local_search (optimization)**

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