dbea-matlab's Introduction
% This is a code for constrained and unconstrained multi/many objective % optimization: Authors Md. Asafuddoula, Tapabrata Ray and Ruhul Sarker % Generates reference directions via systematic sampling. % Population size is defined based on the number of weight vectors % Uses SBX and polynomial mutation to create two children, one of which is % considered. % The objective functions should be positive and remain positive throughout % the search domain. % Intercept update rule: if the norm of (ideal point in current generation % minus ideal point in the last generation is less than tolerance of 1e-7, % the intercepts can reduce). % eps_obj is set to 0: Allign the solutions % eps_obj can be adaptively choosen in which case there will be compromise % in the allignment while it may offer a faster convergence for many % objective optimization problems. % For details, Please see the problem definition file for creation and addition of new problems %Please forecited the following two papers for your reference %[1] Asafuddoula, A., Ray, T. and Sarker, R., “A decomposition based evolutionary algorithm for many objective optimization % with systematic sampling and adaptive epsilon control,” in Proceedings of the Seventh International Conference on % Evolutionary Multi-Criterion Optimization, vol. 7811 Lecture Notes in Computer Science, pp. 417-427, Springer, 2013. %[2] Asafuddoula, M., Ray, T. and Sarker, R., “A decomposition based evolutionary algorithm for many objective optimization” % IEEE Transactions on Evolutionary Computation, In Press, (Accepted 10/06/2014) %%USAGE%% % Add the required path of the nessesary files i.e. path of the core code, path of the problem , for multiobjective mex path %run the multirun_DBEA.m file. %for a specific problem set the problem name(s). multirun_DBEA %Datafile %1. all datafile contains %for i=1 to size of population %alldata=[alldata; gen i pop(i).x pop(i).c pop(i).Fi pop(i).f]; %2. def.stats_all =1 will produce the data for all generations.
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