README file to the software package 'hc-mmas-upqp.tar'This software package was developed in connection with the paper"The Hyper-Cube Framework for Ant Colony Optimization"that is available as a technical report TR/IRIDIA/2003-3, IRIDIA, Universite Libre de Bruxelles, Brussels, BelgiumThe software package is freely availble subject to the GNU General Public Licence, which is included in file gpl.txt.The software package provides a MAX-MIN Ant System implemented in the Hyper-Cube Framework for the application to Unconstrained Binary Quadratic Programming (UBQP). Executable: hc-mmas-ubqpAIMS OF THE SOFTWARE: The aim of the software is rather of 'EDUCATIONAL' character. It shows how to implement a MAX-MIN Ant System in the Hyper-Cube Framework. Nevertheless, the results are close to state-of-the-art.CONTENTSThe GNU General Public Licence:gpl.txtThe main file:hc-mmas-ubqp.cppThe random generator:Random.ccRandom.hA class for keeping solutions to the problem:Solution.cppSolution.hA class for handling CPU time measurements:Timer.ccTimer.hThe ant class:Unconstrained_Ant.cppUnconstrained_Ant.hAdditional routines that are used by the software components above:utilstuff.cpputilstuff.hMakefileSome problem instances from the set by F.Glover and G.Kochenberger:100-10d.blu100-1d.blu100-2d.blu100-3d.blu100-4d.blu200-1e.blu200-2e.blu200-3e.blu200-4e.blu200-5e.blu500-1f.blu500-2f.bluAll the other problem instances are available from the OR-Library (see Internet)COMPILATIONThe software is developed in C++ under Linux, using the GNU 2.96 GCC version.Therefore, we don't guarantee that it works under Windows.Under Linux you just type 'make' and the executables will be produced.USAGEThe main file 'hc-mmas-ubqp.cpp' is documented in a way such that it should bevery easy to understand the code.The executable 'hc-mmas-ubqp' provides the following command line options:Mandatory:-i      : a problem instance-t      : a maximum CPU timeor-maxiter: a maximum number of iterationsOptional:-nants  : the number of ants    (default: 10)-lrate  : the learning rate     (default: 0.05)-n      : the number of trials  (default: 1)-ls     : usage of local search (default: yes)So, an example for running an experiments would be:hc-mmas-upqp -i 200-1e.blu -t 100.0OUTPUTEvery experiment produces the following output. Every trial is started bybegin try <trial_number>Then, each time the algorithm finds a new best solution the output isbest <best_value>   time <cpu_time>Every trial is ended byend try <trail_number>Once all trials are executed the program writes out a line of statistics:statistics <best_found_in_all_trials>  <average_of_bests_found>  <standard_dev_of bests_found><average_time_when_bests_found>  <standard_dev_of_times_when_bests_found>Have fun, and if you have any comments please write to cblum@ulb.ac.beCheers,Christian Blum