We consider the global minimization of smooth functions based solely on function evaluations. Algorithms that achieve the optimal number of function evaluations for a given precision level typically rely on explicitly constructing an approximation of the function which is then minimized with algorithms that have exponential running-time complexity....
Published in Nanophotonics
We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the ...
Published in Foundations of Computing and Decision Sciences
This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degre...
Published in Automation and Remote Control
Multidimensional multiextremal optimization problems and numerical methods for solving them are studied. The objective function is supposed to satisfy the Lipschitz condition with an a priori unknown constant, which is the only general assumption imposed on it. Problems of this type often arise in applications. Two dimensionality reduction approach...
Published in Automation and Remote Control
The DIRECT method solves Lipschitz global optimization problems on a hyperinterval with an unlimited range of Lipschitz constants. We propose an extension of the DIRECT method principles to problems with multiextremal constraints is proposed when two evaluations of functions at the ends of the chosen main diagonals are used at once. We present comp...
In this paper, we combine the concepts of hyper-volume, ant colony optimization and nondominated sorting to develop a novel multi-objective ant colony optimizer for global space trajectory optimization. In particular, this algorithm is first tested on three space trajectory bi-objective test problems: an Earth-Mars transfer, an Earth-Venus transfer...
The performance of lithium-ion batteries will inevitably degrade during the high frequently charging/discharging load applied in electric vehicles. For hybrid electric vehicles, battery aging not only declines the performance and reliability of the battery itself, but it also affects the whole energy efficiency of the vehicle since the engine has t...
Published in Open Computer Science
Advanced optimization algorithms with a variety of configurable parameters become increasingly difficult to apply effectively to solving optimization problems. Appropriate algorithm configuration becomes highly relevant, still remaining a computationally expensive operation. Development of machine learning methods allows to model and predict the ef...
Mixed-integer nonlinear programming (MINLP) is one of the most important classes of mathematical optimization problems that combines difficulties from mixed-integer linear programming and nonlinear programming, namely optimizing over a set that is described by integrality, linear, and nonlinear restrictions. This class of optimization problems is e...
Combined heat and power (CHP) systems are attracting increasing attention for their ability to improve the economics and sustainability of the electricity system. Determining how to best operate these systems is difficult because they can consist of many generating units whose operation is governed by complex nonlinear physics. Mathematical program...
