Global Optimization using MATLAB

Global Optimization provides methods that search for global solutions to problems that contain multiple maxima or minima. The objective of global optimization is to find the globally best solution of models, in the presence of multiple local optima. It includes global search, multistart, pattern search, genetic algorithm, and simulated annealing solvers Genetic algorithm and pattern search solvers support algorithmic customization. Formally, global optimization seeks global solution(s) of a constrained optimization model. Nonlinear models are ubiquitous in many applications, e.g., in advanced engineering design, biotechnology, data analysis, environmental management, financial planning, process control, risk management, scientific modeling, and others.

We at MatlabHomeworkExperts have a team who has helped a number of students pursuing education through regular and online universities, institutes or online Programs. Students assignments are handled by highly qualified and well experienced experts from various countries as per student’s assignment requirements.  We deliver the best and useful Global Optimization projects with source code and proper guidance.

Following is the list of topics under Global Optimization which is prepared after detailed analysis of courses taught in multiple universities across the globe:

  • Calibration of radio propagation models
  • Chemical engineering
  • Computational phylogenetics
  • Curve fitting like non-linear least squares
  • Genetic Algorithm Solver
  • Global Search and Multistart Solvers
  • Mathematical problems
  • Multiobjective Genetic Algorithm Solver
  • Object packing
  • Parallel Computing
  • Pattern Search Solver
  • Protein structure prediction
  • Safety verification, safety engineering
  • Simulated Annealing Solver
  • Solving Optimization Problems
  • Spin glasses
  • Traveling salesman problem
  • Worst-case analysis