Abstract: In this paper a new approach is proposed for the
adaptation of the simulated annealing search in the field of the
Multi-Objective Optimization (MOO). This new approach is called
Multi-Case Multi-Objective Simulated Annealing (MC-MOSA). It
uses some basics of a well-known recent Multi-Objective Simulated
Annealing proposed by Ulungu et al., which is referred in the
literature as U-MOSA. However, some drawbacks of this algorithm
have been found, and are substituted by other ones, especially in
the acceptance decision criterion. The MC-MOSA has shown better
performance than the U-MOSA in the numerical experiments. This
performance is further improved by some other subvariants of the
MC-MOSA, such as Fast-annealing MC-MOSA, Re-annealing MCMOSA
and the Two-Stage annealing MC-MOSA.
Abstract: Zero inflated strict arcsine model is a newly developed
model which is found to be appropriate in modeling overdispersed
count data. In this study, we extend zero inflated strict arcsine model
to zero inflated strict arcsine regression model by taking into
consideration the extra variability caused by extra zeros and
covariates in count data. Maximum likelihood estimation method is
used in estimating the parameters for this zero inflated strict arcsine
regression model.
Abstract: In the modern manufacturing systems, the use of
thermal cutting techniques using oxyfuel, plasma and laser have
become indispensable for the shape forming of high quality complex
components; however, the conventional chip removal production
techniques still have its widespread space in the manufacturing
industry. Both these types of machining operations require the
positioning of end effector tool at the edge where the cutting process
commences. This repositioning of the cutting tool in every machining
operation is repeated several times and is termed as non-productive
time or airtime motion. Minimization of this non-productive
machining time plays an important role in mass production with high
speed machining. As, the tool moves from one region to the other by
rapid movement and visits a meticulous region once in the whole
operation, hence the non-productive time can be minimized by
synchronizing the tool movements. In this work, this problem is
being formulated as a general travelling salesman problem (TSP) and
a genetic algorithm approach has been applied to solve the same. For
improving the efficiency of the algorithm, the GA has been
hybridized with a noble special heuristic and simulating annealing
(SA). In the present work a novel heuristic in the combination of GA
has been developed for synchronization of toolpath movements
during repositioning of the tool. A comparative analysis of new Meta
heuristic techniques with simple genetic algorithm has been
performed. The proposed metaheuristic approach shows better
performance than simple genetic algorithm for minimization of nonproductive
toolpath length. Also, the results obtained with the help of
hybrid simulated annealing genetic algorithm (HSAGA) are also
found better than the results using simple genetic algorithm only.