CHAPTER-3 SOFT COMPUTING TECHNIQUES - …

1 soft COMPUTING TECHNIQUES 11 CHAPTER-3 soft COMPUTING TECHNIQUES soft COMPUTING is the fusion of methodologies that were designed to model and enable solutions to real world problems, which are not modeled or too difficult to model, mathematically. soft COMPUTING is a consortium of methodologies that works synergistically and provides, in one form or another, flexible information processing capability for handling real-life ambiguous situations [45]. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions [35].

2 The guiding principle is to devise methods of computation that lead to an acceptable solution at low cost, by seeking for an approximate solution to an imprecisely or precisely formulated problem [46]. soft COMPUTING differs from conventional (hard) COMPUTING . Unlike hard COMPUTING , it is tolerant of imprecision, uncertainty, partial truth and approximation. In effect, the role model for soft COMPUTING is the human mind. soft COMPUTING is basically optimization technique to find solution of problems which are very hard to answer.

3 OPTIMIZATION It is the process of making something better. Optimization is the process of adjusting the inputs to find the minimum or maximum output or result [40]. The Optimization Process is shown in figure Figure Optimization Process. Optimization is the mechanism by which one finds the maximum or minimum value of a function or process. This mechanism is used in fields such as physics, chemistry, economics, and engineering where the goal is to maximize efficiency, Input or Variables Function or Process Output or Cost soft COMPUTING TECHNIQUES 12 production or some other measure.

4 Optimization can refer to either minimization or maximization; maximization of a function f is equivalent to minimization of the opposite of this function f. An engineer or scientist conjures up a new idea and optimization improves on that idea. Optimization consists in trying variations on an initial concept and using the information gained to improve on the idea. A computer is the perfect tool for optimization as long as the idea or variable influencing the idea can be input in electronic format. Feed the computer some data and out comes the solution.

5 COMBINATORIAL OPTIMIZATION TECHNIQUES Testing problem belongs to the some type of combinatorial optimization problems. The TECHNIQUES used to tackle combinatorial optimization problems can be classified in two general category, firstly, the exact methods and secondly the approximate (heuristic) methods [41]. Although exact methods granted solution to the problem in hand but not appropriate for real life problems as it requires large computation time seek because of their complex nature, hence the resolution by exact methods is not realistic for large problems, justifying the use of powerful heuristic and meta-heuristics methods.

6 For practical use heuristic methods seek to find high quality solutions (not necessarily optimal) within reasonable computation times [41]. Another type of methods is meta-heuristics which have been applied successfully on large and real life complex problems over the years by different researchers has provided fruitful results [42 - 43]. Classification of common search methodologies are shown in figures and NEURAL NETWORKS (NNS) There are millions of very simple processing elements or neurons in the brain, linked together in a massively parallel manner.

7 This is believed to be responsible for the human intelligence and discriminating power [35]. Neural Networks are developed to try to achieve biological system type performance using a dense interconnection of simple processing elements analogous to biological neurons. Neural Networks are information driven rather than data driven [47]. Typically, there are at least two layers, an input layer and an output layer. One of the most common networks is the Back Propagation Network (BPN) which consists of an input layer, and an output layer with one or more intermediate hidden layers [41].

8 soft COMPUTING TECHNIQUES 13 Neural Networks are trained to perform a particular function by adjusting the values of the connections (weights) between elements using a set of examples before they can be employed to the actual problem. Commonly neural networks are adjusted, or trained, so that a particular input leads to a specific target output [44] which is shown in figure The method used to generate the examples to train the network and the training algorithm employed has a significant impact on the performance of the neural network-based model.

9 One of the training algorithms used is the Back-Propagation (BP) algorithm. This algorithm aims to reduce the deviation between the desired objective function value and the actual objective function value [41]. Figure Classifications of Common Search Methodologies Linear Quadratic Non LinearLocal Method Global MethodChemical Method Meta HeuristicOptimization Continuous Combinatorial Approximate MethodExact Method Heuristic Population Based Neighborhood Based soft COMPUTING TECHNIQUES 14 Figure Classifications of Common Meta-heuristics.

10 The performance of this very slow and mostly trapped in local optima, there are another training algorithms which has faster coverage speed and tries to avoid to struck in local optima like Delta-bar-delta (DBD). MetaheuristicsNeighborhood Based Algorithm Population Based Algorithm Simulated Annealing Tabu SearchAnt Colony Optimization Swarm Intelligence Evolutionary computation Evolutionary programming GeneticAlgorithm Genetic Programming Evolutionary Strategies Particle Swarm Optimization Differential Algorithm soft COMPUTING TECHNIQUES 15 Figure Neural Networks Limitations The major issues of concern today are the scalability problem, testing.