Browsing by Author "Pedamallu, Chandra Sekhar"

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Citation - WoS: 2
Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization
(Springer-Verlag Berlin, 2008) Pedamallu, Chandra Sekhar; Özdamar, Linet
The continuous Constrained Optimization Problem (COP) often occurs in industrial applications. In this study, we compare three novel algorithms developed for solving the COP. The first approach consists of an Interval Partitioning Algorithm (IPA) that is exhaustive in covering the whole feasible space. IPA has the capability of discarding sub-spaces that are sub-optimal and/or infeasible, similar to available Branch and Bound techniques. The difference of IPA lies in its use of Interval Arithmetic rather than conventional bounding techniques described in the literature. The second approach tested here is the novel dual-sequence Simulated Annealing (SA) algorithm that eliminates the use of penalties for constraint handling. Here, we also introduce a hybrid algorithm that integrates SA in IPA (IPA-SA) and compare its performance with stand-alone SA and IPA algorithms. All three methods have a local COP solver, Feasible Sequential Quadratic Programming (FSQP) incorporated so as to identify feasible stationary points. The performances of these three methods are tested on a suite of COP benchmarks and the results are discussed.
Citation - WoS: 1
Citation - Scopus: 1
Efficient Interval Partitioning - Local Search Collaboration for Constraint Satisfaction
(Pergamon-Elsevier Science Ltd, 2008) Pedamallu, Chandra Sekhar; Ozdamar, Linet; Ceberio, Martine
In this article, a cooperative solution methodology that integrates interval partitioning (IP) algorithms with a local search, feasible sequential quadratic programming (FSQP), is presented as a technique to enhance the solving of continuous constraint satisfaction problems (continuous CSP). FSQP is invoked using a special search tree management system developed to increase search efficiency in finding feasible solutions. In this framework, we introduce a new symbolic method for selecting the subdivision directions that targets immediate reduction of the uncertainty related to constraint infeasibility in child boxes. This subdivision method is compared against two previously established partitioning rules (also parallelized in a similar manner) used in the interval literature and shown to improve the efficiency of IP. Further, the proposed tree management system is compared with tree management approaches that are classically used in IP. The whole method is compared with published results of established symbolic-numeric methods for solving CSP on a number of state-of-the-art benchmarks. (c) 2006 Elsevier Ltd. All rights reserved.
Citation - Scopus: 2
An Interval Partitioning Approach for Continuous Constrained Optimization
(Springer, 2007) Pedamallu, Chandra Sekhar; Ozdamar, Linet; Csendes, Tibor
Constrained Optimization Problems (COP's) are encountered in many scientific fields concerned with industrial applications such as kinematics, chemical process optimization, molecular design, etc. When non-linear relationships among variables are defined by problem constraints resulting in non-convex feasible sets, the problem of identifying feasible solutions may become very hard. Consequently, finding the location of the global optimum in the COP is more difficult as compared to bound-constrained global optimization problems. This chapter proposes a new interval partitioning method for solving the COP. The proposed approach involves a new subdivision direction selection method as well as an adaptive search tree framework where nodes (boxes defining different variable domains) are explored using a restricted hybrid depth-first and best-first branching strategy. This hybrid approach is also used for activating local search in boxes with the aim of identifying different feasible stationary points. The proposed search tree management approach improves the convergence speed of the interval partitioning method that is also supported by the new parallel subdivision direction selection rule (used in selecting the variables to be partitioned in a given box). This rule targets directly the uncertainty degrees of constraints (with respect to feasibility) and the uncertainty degree of the objective function (with respect to optimality). Reducing these uncertainties as such results in the early and reliable detection of infeasible and sub-optimal boxes, thereby diminishing the number of boxes to be assessed. Consequently, chances of identifying local stationary points during the early stages of the search increase. The effectiveness of the proposed interval partitioning algorithm is illustrated on several practical application problems and compared with professional commercial local and global solvers. Empirical results show that the presented new approach is as good as available COP solvers.
Citation - WoS: 43
Citation - Scopus: 58
Investigating a Hybrid Simulated Annealing and Local Search Algorithm for Constrained Optimization
(Elsevier, 2008) Pedamallu, Chandra Sekhar; Ozdamar, Linet
Constrained Optimization Problems (COP) often take place in many practical applications such as kinematics, chemical process optimization, power systems and so on. These problems are challenging in terms of identifying feasible solutions when constraints are non-linear and non-convex. Therefore, finding the location of the global optimum in the non-convex COP is more difficult as compared to non-convex bound-constrained global optimization problems. This paper proposes a Hybrid Simulated Annealing method (HSA), for solving the general COP. HSA has features that address both feasibility and optimality issues and here, it is supported by a local search procedure, Feasible Sequential Quadratic Programming (FSQP). We develop two versions of HSA. The first version (HSAP) incorporates penalty methods for constraint handling and the second one (HSAD) eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently. Numerical experiments show that the second version is more reliable in the worst case performance. (C) 2006 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 4
Symbolic Interval Inference Approach for Subdivision Direction Selection in Interval Partitioning Algorithms
(Springer, 2007) Pedamallu, Chandra Sekhar; Özdamar, Linet; Csendes, Tibor
In bound constrained global optimization problems, partitioning methods utilizing Interval Arithmetic are powerful techniques that produce reliable results. Subdivision direction selection is a major component of partitioning algorithms and it plays an important role in convergence speed. Here, we propose a new subdivision direction selection scheme that uses symbolic computing in interpreting interval arithmetic operations. We call this approach symbolic interval inference approach (SIIA). SIIA targets the reduction of interval bounds of pending boxes directly by identifying the major impact variables and re-partitioning them in the next iteration. This approach speeds up the interval partitioning algorithm (IPA) because it targets the pending status of sibling boxes produced. The proposed SIIA enables multi-section of two major impact variables at a time. The efficiency of SIIA is illustrated on well-known bound constrained test functions and compared with established subdivision direction selection methods from the literature.