Global optimization of mixed-integer nonlinear bilevel optimization problems is addressed using a novel technique. For problems where integer variables participate in both inner and outer problems, the outer level may involve general mixed-integer nonlinear functions. The inner level may involve functions that are mixed-integer nonlinear in outer variables, linear, polynomial, or multilinear in...

This paper presents a robust hybrid improved dolphin echolocation and ant colony optimization algorithm (IDEACO) for optimization of truss structures with discrete sizing variables. The dolphin echolocation (DE) is inspired by the navigation and hunting behavior of dolphins. An improved version of dolphin echolocation (IDE), as the main engine, is proposed and uses the positive attributes of an...

In this work, we report the experimental implementation of a Quantitative Feedback Theory (QFT) based robust controller, designed online using hybrid global optimization and constraint propagation techniques. The hybrid global optimization combines interval global optimization and nonlinear local optimization methods. The constraint propagation techniques are very effective in discarding infeas...

Data requests ro mulriaiztabase (MDBSs) are posed through non-procedural languages such as SQL and for a global data request optimization must be performed to achieve good system performance. However, the issues are ofren complicated in multidatabases, due to addirional issues arising because of the autonomy and heterogeneity restrictions of the independent local DBMSs. The data translation pro...

Stochastic global optimization methods are methods for solving a global optimization problem incorporating probabilistic (stochastic) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself, or in both. Global optimization is a very important part of applied mathematics and computer science. The importance of global optimization is primar...

The class of POP (Polynomial Optimization Problems) covers a wide rang of optimization problems such as 0− 1 integer linear and quadratic programs, nonconvex quadratic programs and bilinear matrix inequalities. In this paper, we review some methods on solving the unconstraint case: minimize a real-valued polynomial p(x) : R → R, as well the constraint case: minimize p(x) on a semialgebraic set ...

Swarm intelligence algorithms have been succesfully applied to hard optimization problems. Seeker optimization algorithm is one of the latest members of that class of metaheuristics and it has not yet been thorougly researched. Since the early versions of this algorithm were less succesful with multimodal functions, we propose in this paper hybridization of the seeker optimization algorithm wit...

Optimization is about inferring the location of the optimum of a function. An information-optimal optimizer should thus aim to collapse its belief about the location of the optimum towards a point-distribution, as fast as possible. But the state of the art rarely addresses this inference problem. Instead, it usually relies on some heuristic predicting function optima, then evaluates at the maxi...