Novel Nature Inspired Meta-heuristics algorithms based Optimization Toolbox

Paper #:
  • 2018-01-0093

  • 2018-04-03
Now a days the quantities of data available are very large in data science. The inner analysis of big-data leads to better decisions, but the big data itself cannot be processed as such because of its huge size. Hence the optimization of the big data is the most required and the challenging aspect in any industry. For big-data, the traditional optimization algorithms will fail because of the huge processing time and they start with some initial assumptions, so in order to optimize the big-data we need some special algorithms to do the job. The nature inspired Meta-heuristic algorithms can be used to solve problems of varying complexity from simple polynomial complexity to complex non-polynomial hard problems. Currently there is no single toolbox is available, which uses more than 20 different meta-heuristic algorithms to solve the optimization problem. The proposed system is a toolbox which takes the data from the user either directly from the stored datasheet or from an interfaced hardware for the specified fitness function and optimizes the data using the user selected algorithms. The well known meta-heuristics algorithms such as Ant Colony algorithm, Artificial Bee Colony algorithm, Cultural algorithm etc are part of toolbox. For the entered input parameters for the algorithm the data is optimized and displayed on the result console. More number of algorithms in the toolbox provides the user the diversity in selecting algorithms depends on their performance. The performance of the toolbox is evaluated in terms of time required for the optimization and also on the number of iterations taken to converge to an optimum solution since these are non-deterministic solutions.
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