Author
A. Ravindran
Bio: A. Ravindran is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Engineering optimization & Linear programming. The author has an hindex of 3, co-authored 6 publications receiving 1590 citations.
Papers
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Book•
06 Sep 1983
TL;DR: This book discusses the application of Optimization in Engineering and its applications in Linear Programming, as well as some of the techniques used to design and implement these programs.
Abstract: Functions of a Single Variable. Functions of Several Variables. Linear Programming. Constrained Optimality Criteria. Transformation Methods. Constrained Direct Search. Linearization Methods for Constrained Problems. Direction--Generation Methods Based on Linearization. Quadratic Approximation Methods for Constrained Problems. Structured Problems and Algorithms. Comparison of Constrained Optimization Methods. Strategies for Optimization Studies. Engineering Case Studies. Appendixes. Author and Subject Indexes.
1,142 citations
Book•
01 Jan 1976
TL;DR: The Nature of Operations Research Linear Programming Network Analysis Advanced Topics in Linear Programming Decision Analysis Random Processes Queueing Models Inventory Models Simulation Dynamic Programming Nonlinear Programming Appendices Index as mentioned in this paper
Abstract: The Nature of Operations Research Linear Programming Network Analysis Advanced Topics in Linear Programming Decision Analysis Random Processes Queueing Models Inventory Models Simulation Dynamic Programming Nonlinear Programming Appendices Index
373 citations
23 Mar 2007
92 citations
23 Mar 2007
3 citations
23 Mar 2007
2 citations
Cited by
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TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
3,495 citations
TL;DR: The impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented and the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods.
1,782 citations
TL;DR: A new harmony search (HS) meta-heuristic algorithm-based approach for engineering optimization problems with continuous design variables conceptualized using the musical process of searching for a perfect state of harmony using a stochastic random search instead of a gradient search.
1,714 citations
TL;DR: The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area and the optimal solutions obtained are mostly far better than the best solutions obtained by the existing methods.
Abstract: In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Levy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.
1,701 citations
01 Jan 1995
TL;DR: This paper presents algorithms for global optimization of mixed-integer nonlinear programs using the Reformulation-Linearization/Convexification Technique (RLT) and an introduction to dynamical search.
Abstract: Preface. 1. Tight relaxations for nonconvex optimization problems using the Reformulation-Linearization/Convexification Technique (RLT) H.D. Sherali. 2. Exact algorithms for global optimization of mixed-integer nonlinear programs M. Tawarmalani, N.V. Sahinidis. 3. Algorithms for global optimization and discrete problems based on methods for local optimization W. Murray, Kien-Ming Ng. 4. An introduction to dynamical search L. Pronzato, et al. 5. Two-phase methods for global optimization F. Schoen. 6. Simulated annealing algorithms for continuous global optimization M. Locatelli. 7. Stochastic Adaptive Search G.R. Wood, Z.B. Zabinsky. 8. Implementation of Stochastic Adaptive Search with Hit-and-Run as a generator Z.B. Zabinsky, G.R. Wood. 9. Genetic algorithms J.E. Smith. 10. Dataflow learning in coupled lattices: an application to artificial neural networks J.C. Principe, et al. 11. Taboo Search: an approach to the multiple-minima problem for continuous functions D. Cvijovic, J. Klinowski. 12. Recent advances in the direct methods of X-ray crystallography H.A. Hauptman. 13. Deformation methods of global optimization in chemistry and physics L. Piela. 14. Experimental analysis of algorithms C.C. McGeoch. 15. Global optimization: software, test problems, and applications J.D. Pinter.
1,546 citations