The most methods of Multiple Criteria Decision Making (MCDM)
will cause acomputational burden tremendously in applying to the
combinatorial optimization problem. Besides, the most methods of
MCDM ignore the new-coming information and cannot deal the
weight of decision maker dynamically. Using the parallel-
searching, this research develops a Genetic Algorithm (GA) to
overcome theseshortages. There are three primary parts in this
research: developing a dynamic weight assessing method, proving
the effectiveness of GA in the traveling salesman problem (TSP),
developing a method to overcome the shortages above.
Theimportant results are listed in the below.(1) To develop a
weight assessingmethod by Habitual Domain.(2) To analyze the
genetic Crossover operator in TSP,and to classify and implement
23 relative operators, and to propose 3 improving operators.(3)
To develop the "Doubly-Nearest-Available neighborhood" crossover
operator, and to compare its performance with others by 24 well-
knownnetworks.(4) To propose the concept of template database
for improving thequality of crossover.(5) To improve a Space-
Filling Curve of Chaos Theoryas a quick procedure of tour
initialization.(6) To propose a Genetic Algorithm to solve the
TSP by combining Doubly-Nearest-Available neighborhoodand
template database. And, to measure its performance by 35
networks that are from 48 to 657 nodes and are obtained from
TSPLIB. The result is excellent: this method finds 27 exact
solutions in 35 networks with only 0.03%error rate in
average.(7) To combine the weight assessing method and
theparallel-searching of GA, and propose a multiple-objectives
GA. The mainproperties are (a) modifying weight dynamically with
the results in searching process; (b) and reducing to
computational requirement to the levelof single-objective; (c)
and computing the weight directly.(8) To apply theabove method
to the post-office in Taipei, and to demonstrate the detail by a
numeric example.(9) To analyze and write the computer language
ofmethods of combinatorial optimization problem and TSP, and to
review thehundred''s reference of them. And to build a homepage
in the Internet forsupplying the source code and relative
inquiry service.