8 research outputs found
Recommended from our members
Constrained blackbox optimization: The SEARCH perspective
Search and optimization in the context of blackbox objective function evaluation subject to blackbox constraints satisfaction is the thesis of this work. The SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework introduced by Kargupta (1995) offered an alternate perspective of blackbox optimization in terms of relations, classes, and partial ordering. The primary motivation comes from the observation that sampling in blackbox optimization is essentially an inductive process and in the absence of any relation among the members of the search space, induction is no better than enumeration. SEARCH also offers conditions for polynomial complexity search and bounds on sample complexity using its ordinal, probabilistic, and approximate framework. In this work the authors extend the SEARCH framework to tackle constrained blackbox optimization problems. The methodology aims at characterizing the search domain into feasible and infeasible relations among which the feasible relations can be explored further to optimize an objective function. Both -- objective function and constraints -- can be in the form of blackboxes. The authors derive results for bounds on sample complexity. They demonstrate their methodology on several benchmark problems
Recommended from our members
Solution of the optimal plant location and sizing problem using simulated annealing and genetic algorithms
In the optimal plant location and sizing problem it is desired to optimize cost function involving plant sizes, locations, and production schedules in the face of supply-demand and plant capacity constraints. We will use simulated annealing (SA) and a genetic algorithm (GA) to solve this problem. We will compare these techniques with respect to computational expenses, constraint handling capabilities, and the quality of the solution obtained in general. Simulated Annealing is a combinatorial stochastic optimization technique which has been shown to be effective in obtaining fast suboptimal solutions for computationally, hard problems. The technique is especially attractive since solutions are obtained in polynomial time for problems where an exhaustive search for the global optimum would require exponential time. We propose a synergy between the cluster analysis technique, popular in classical stochastic global optimization, and the GA to accomplish global optimization. This synergy minimizes redundant searches around local optima and enhances the capable it of the GA to explore new areas in the search space
Recommended from our members
Adaptive, predictive controller for optimal process control
One can derive a model for use in a Model Predictive Controller (MPC) from first principles or from experimental data. Until recently, both methods failed for all but the simplest processes. First principles are almost always incomplete and fitting to experimental data fails for dimensions greater than one as well as for non-linear cases. Several authors have suggested the use of a neural network to fit the experimental data to a multi-dimensional and/or non-linear model. Most networks, however, use simple sigmoid functions and backpropagation for fitting. Training of these networks generally requires large amounts of data and, consequently, very long training times. In 1993 we reported on the tuning and optimization of a negative ion source using a special neural network[2]. One of the properties of this network (CNLSnet), a modified radial basis function network, is that it is able to fit data with few basis functions. Another is that its training is linear resulting in guaranteed convergence and rapid training. We found the training to be rapid enough to support real-time control. This work has been extended to incorporate this network into an MPC using the model built by the network for predictive control. This controller has shown some remarkable capabilities in such non-linear applications as continuous stirred exothermic tank reactors and high-purity fractional distillation columns[3]. The controller is able not only to build an appropriate model from operating data but also to thin the network continuously so that the model adapts to changing plant conditions. The controller is discussed as well as its possible use in various of the difficult control problems that face this community