ADVANCED OPTIMIZATION USING BIG DATA TECHNIQUES

ADVANCED OPTIMIZATION USING BIG DATA TECHNIQUESThis book develops advanced methods and optimization through MATLAB software. The Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. Simulated annealing is a method for solving unconstrained and boundconstrained optimization problems.

This book develops advanced methods and optimization through MATLAB software. The Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. Simulated annealing is a method for solving unconstrained and boundconstrained optimization problems. The method models the physical process of heating a material and then slowly lowering the temperature to decrease defects, thus minimizing the system energy. Multiobjective optimization is concerned with the minimization of a vector of objectives that can be the subject of a number of constraints or bounds. In Big Data problems Parallel Processing is an attractive way to speed optimization algorithms. To use parallel processing, you must have a Parallel Computing Toolbox license, and have a parallel worker pool (parpool).