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In addition to this, a Fibonacci search-based method is presented as a fast way to explore the solutions space and reach the minimum point without falling in the need to compute the error function at thousands of different solutions.
As the pathfinding capabilities improve, so do the number of solutions that can potentially be generated, and with it the challenge of providing the user with tools to explore the solutions that can number in the thousands and identify pathways of interest.
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This allows us to explore the solution space efficiently.
To explore the solution space represented by the SOP model a holonic interaction protocol is presented.
They explore the solution space and discover a set of routes.
In order to explore the solution space, local search procedures are applied.
Then, they slightly mutated in order to further explore the solution space nearby the good (fittest) algorithms already found.
They were meta-heuristic algorithms which explore the solution area to find optimality with diversification and intensification search (Talbi 2009).
Therefore, exhaustive search algorithms cannot be used and intelligent techniques must be developed to explore the solution space efficiently.
R m should be as low as possible to make a slight change in good solutions and to explore the solution space around them.
The stochastic search methods require significantly more computational capacity, explore the solution space more fully, and generate solutions that are largely insensitive to the initial guess.
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