| dc.description.abstract | The optimization of fuzzy membership function is a solution that is widely applied to
determine the proper design of membership functions and optimal. In this research the
optimization based Modified Particle Swarm Optimization (MPSO) algorithm is
applied to optimize fuzzy membership functions. There are two methods MPSO which
applied the method of Linear Decreasing Inertia Weight (LDIW) and constriction
Factor Method (FCM). Each of these methods is tested with 10 trials on two types of
particle number, namely 50 and 20 particles. From the test results showed that the
amount of the same particle, CFM obtains the global best fitness value which is more
optimal than LDIW method. The test is performed as many as 10 and apply 50
particles, the first experiment obtained the global best fitness value, namely 1.4;1.4;
2.36 and 3.28 for each productivity variable, isolation, social relations and
accessibility. The test is performed as many as 10 attempts and applied 20 particles
obtained global best fitness value namely 2.34; 2.40; 2.J7 and 3.36 for each variable.
On the other hand CFM obtain faster results convergence than the method of LIDW.
The experiment at 100 swarm, LDIW method is to find the global best fitness on
swarm 91, 84, 54 and 38 for each variable, while the CFM method finds the global
best fitness on the swarm 81. 23. 34 and 23. | en_US |
