For a given constraint there may also exist several minimal surfaces with different areas ( for example, see minimal surface of revolution ) : the standard definitions only relate to a local optimum, not a global optimum.
32.
In applied mathematics and computer science, a "'local optimum "'of an optimization problem is a solution that is optimal ( either all possible solutions, not just those in a particular neighborhood of values.
33.
Slightly more complex algorithms rely on local optimization to reach a local optimum of a placement evaluation function in each iteration placement of a single label is moved to another position, and if it improves the result, the move is preserved.
34.
It's only guaranteed to find the global optimum solution if it's also the only local optimum, which it tends not to be in interesting problems .-- talk ) 20 : 49, 29 October 2012 ( UTC)
35.
Locust swarms are based on a " devour and move on " strategy after a sub-swarm " devours " a relatively small region of the search space ( to find a local optimum ) scouts are deployed to look for new promising regions to " move on ".
36.
This school of thought has been prevalent since the inception of PSO . This school of thought contends that the PSO algorithm and its parameters must be chosen so as to properly balance between exploration and exploitation to avoid premature convergence to a local optimum yet still ensure a good rate of below.
37.
Note that'iterative repair', unlike the'backtracking'search outlined above, does not guarantee a solution : like all hillclimbing ( i . e ., greedy ) procedures, it may get stuck on a local optimum ( in which case the algorithm may be restarted with a different initial configuration ).
38.
Hill climbing is good for finding a local optimum ( a solution that cannot be improved by considering a neighbouring configuration ) but it is not necessarily guaranteed to find the best possible solution ( the global optimum ) out of all possible solutions ( the convex problems, hill-climbing " is " optimal.
39.
One of the significant ways in which evolution is " not " intelligent is that while it's very good at blindly finding its way to a local optimum or maximum, it's very bad at stepping back, getting the " big picture ", and trying a completely different approach that would let it reach some other, and much higher, optimum or maximum . ( Although on the other hand, us supposedly intelligent humans aren't always so hot at doing this, either . ) talk ) 05 : 51, 8 March 2006 ( UTC)
