An optimization protocol for solving the optimization problem in a distributed manner by application of a nonlinear incremental cost (IC) consensus method is proposed. The analysis for the proposed ...

Optimization problems concern exploration of the best possible solutions to a given problem. The feasible solutions are termed good or bad based on the respective values of the objective function. For ...

Real world objective functions often produce two types of uncertain output: noise and imprecision. While there is a distinct difference between both types, most optimization algorithms treat them the ...

By using the penalty function method with objective parameters, the paper presents an interactive algorithm to solve the inequality ... Z. Meng, R. Shen and M. Jiang, "An Objective Penalty Functions ...

pyGKLS is a Python wrapper for the GKLS generator of global optimization test functions (Giavano et al., 2003). ... Add a description, image, and links to the objective-function-optimization topic ...

My attempt was to combine the principles from tutorials/papers on Robust multi-objective Bayesian optimization under input noise and Risk averse Bayesian optimization with environmental ...

Multiobjective programming is a branch of optimization that deals with problems that have more than one objective function to be minimized or maximized. For example, you might want to design a ...

Learn how to choose the best python optimization library for your project based on problem type, algorithm choice, and interface and usability. Compare popular and useful libraries and tools.

By using the penalty function method with objective parameters, the paper presents an interactive algorithm to solve the inequality constrained multi-objective programming (MP). The MP is transformed ...