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 ...

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 ...

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 ...

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