One common problem encountered is the complexity of how land mines are distributed in a given space. In a mathematical sense, this can be rephrased as what is the optimal or fairest possible distribution of land mines in order to give the highest unpredictability of locating such weapons in a given space. The space is large but quantifiable.
One possible solution is the Simulated Annealing Algorithm. Annealing in metallurgy is the process of heating and controlled cooling in order to strengthen further the metal. So in the algorithm, it is basically providing a series of solutions until an optimal solution was reached with the goal of evenly distributing the land mines in such a unpredictable pattern.
Let us just imagine the mine field as rectangle where we need to distribute land mines which in this case are the eddystone beacons in a random pattern where the transmission range never overlaps. And to arrange them in an optimized way where the enemy will not be able to easily avoid the landmines to avoid getting eliminated we will use Simulated Annealing Algorithm.
The High Level Implementation
Based on the above picture, we have 4 eddystone beacons at hand and our main goal is to arrange them so that the enemy will have a very hard time in surviving by avoiding the landmines which when he stays within range, of the landmine, it explodes. Arranging the landmines can have a lot of possibilities. This problem is almost the same as the Travelling Salesman Problem nut our goal is generate a solution where the salesman has to travel is the longest possible time. Yes, there should be a way for the selesman to at least survive the test. To be continued...
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