Optimization algorithms are mathematical methods that search for the optimal or near-optimal solution to a problem, given a set of variables, constraints, and objectives. Optimization algorithms ...

Two deterministic optimization algorithms (mixed-integer nonlinear programming and heuristic algorithm) are compared to three nondeterministic approaches (harmony search, differential evolution, and ...

Preparation for Using Optimization Algorithms It is rare that a problem is submitted to an optimization algorithm "as is." By making a few changes in your problem, you can reduce its complexity, that ...

Due to the NP -hardness of many machine learning problems such as clustering, decision tree, and neural network, one primary belief is that solving ML problems to global optimality is computationally ...

A deterministic optimization framework is developed to compare the performance of two algorithms: (1) the improved genetic algorithm and (2) efficient stochastic annealing. Uncertainties are ...

One deterministic optimization algorithm (Mixed Integer Nonlinear Programming), is compared to three non-deterministic approaches (Harmony Search, Differential Evolution and Genetic Algorithm). The ...