Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption
This paper introduces a novel methodology for the global optimization of general constrained grey-box problems. A grey-box problem may contain a combination of black-box constraints and constraints with a known functional form. The novel features of ...
Global optimization of non-convex generalized disjunctive programs: a review on reformulations and relaxation techniques
In this paper we present a review on the latest advances in logic-based solution methods for the global optimization of non-convex generalized disjunctive programs. Considering that the performance of these methods relies on the quality of the ...
Bounds tightening based on optimality conditions for nonconvex box-constrained optimization
First-order optimality conditions have been extensively studied for the development of algorithms for identifying locally optimal solutions. In this work, we propose two novel methods that directly exploit these conditions to expedite the solution of ...
ParEGO extensions for multi-objective optimization of expensive evaluation functions
This paper deals with multi-objective optimization in the case of expensive objective functions. Such a problem arises frequently in engineering applications where the main purpose is to find a set of optimal solutions in a limited global processing ...
A Bayesian approach to constrained single- and multi-objective optimization
This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a ...
Interactive model-based search with reactive resource allocation
We revisit the interactive model-based approach to global optimization proposed in Wang and Garcia (J Glob Optim 61(3):479---495, 2015) in which parallel threads independently execute a model-based search method and periodically interact through a ...
Packing ellipsoids into volume-minimizing rectangular boxes
A set of tri-axial ellipsoids, with given semi-axes, is to be packed into a rectangular box; its widths, lengths and height are subject to lower and upper bounds. We want to minimize the volume of this box and seek an overlap-free placement of the ...
On Laplacian spectra of parametric families of closely connected networks with application to cooperative control
In this paper, we introduce mathematical models for studying a supernetwork that is comprised of closely connected groups of subnetworks. For several related classes of such supernetworks, we explicitly derive an analytical representation of their ...
Optimal transport and a bilevel location-allocation problem
In this paper a two-stage optimization model is studied to find the optimal location of new facilities and the optimal partition of the consumers (location-allocation problem). The social planner minimizes the social costs, i.e. the fixed costs plus the ...
Supply chain performance assessment and supplier and component importance identification in a general competitive multitiered supply chain network model
In this paper, we develop a multitiered competitive supply chain network game theory model, which includes the supplier tier. The firms are differentiated by brands and can produce their own components, as reflected by their capacities, and/or obtain ...
Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine
This paper deals with serial-batching scheduling problems with the effects of deterioration and learning, where time-dependent setup time is also considered. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then ...
Dynamic algorithm selection for pareto optimal set approximation
This paper presents a meta-algorithm for approximating the Pareto optimal set of costly black-box multiobjective optimization problems given a limited number of objective function evaluations. The key idea is to switch among different algorithms during ...
A diverse human learning optimization algorithm
Human Learning Optimization is a simple but efficient meta-heuristic algorithm in which three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to efficiently search ...
A modified active set algorithm for transportation discrete network design bi-level problem
Transportation discrete network design problem (DNDP) is about how to modify an existing network of roads and highways in order to improve its total system travel time, and the candidate road building or expansion plan can only be added as a whole. DNDP ...
A Kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points
In many engineering optimization problems, the objective and the constraints which come from complex analytical models are often black-box functions with extensive computational effort. In this case, it is necessary for optimization process to use ...
Local search algorithm for universal facility location problem with linear penalties
The universal facility location problem generalizes several classical facility location problems, such as the uncapacitated facility location problem and the capacitated location problem (both hard and soft capacities). In the universal facility ...
Some feasibility sampling procedures in interval methods for constrained global optimization
Three feasibility sampling procedures are developed as add-on acceleration strategies in interval methods for solving global optimization problem over a bounded interval domain subject to one or two additional linear constraints. The main features of ...
An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems
The huge computational overhead is the main challenge in the application of community based optimization methods, such as multi-objective particle swarm optimization and multi-objective genetic algorithm, to deal with the multi-objective optimization ...
Application of Reduced-set Pareto-Lipschitzian Optimization to truss optimization
In this paper, a recently proposed global Lipschitz optimization algorithm Pareto-Lipschitzian Optimization with Reduced-set (PLOR) is further developed, investigated and applied to truss optimization problems. Partition patterns of the PLOR algorithm ...