Advanced Approaches to Solve Optimization Problems
The continuous increase in demand towards lowering production costs to withstand cutthroat competition has prompted researchers to look for rigorous methods of decision making. As search for best has always fascinated mankind, operations or strategies have been attempted and devised for searching optimum solutions for variety of problems in all branches of activities perceived by logic or intitution or both. With this motivation in this work novel fuzzy machine learning algorithms are proposed to develop optimum solutions for several optimization problems. The emphasis of proposed methodologies is given on handling data sets which are large both in size and dimension and involves classes that are overlapping, intractable and have non-linear boundaries. Several strategies based on data reduction, dimensionality reduction, active learning efficient search heuristics are employed for dealing with scaling issues The problems handle linguistic input and ambiguous output decision, learning of overlapping and intractable class structures, selection of optimal parameters and discovering human comprehensible knowledge in form of linguistic rules. The different features of methodologies along with comparisons with those of related ones are demonstrated extensively on different real life data sets. The experimental data have been considered from variety of domains. The superiority of models over the benchmark are found to be effective and significant.