Manual Theory of Multiobjective Optimization (Mathematics in Science and Engineering)

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Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms

Jahn J. Jittorntrum K. Kim I. Multidisciplinary Optim.

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Mathematical optimization - Wikipedia

Marglin S. Miettinen K. Nishizaki I. Osman M. Pascoletti A. Prohaska, J. Diplomarbeit, Univ. Ruzika S. Sawaragi Y. Number in Mathematics in science and engineering. Schandl B. Shi X. Multi-Criteria Decis. Staib T. Teng C.

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Tuy H. Global Optim. Free Shipping Free global shipping No minimum order. Best operator approximation, Non-Lagrange interpolation, Generic Karhunen-Loeve transform Generalised low-rank matrix approximation Optimal data compression Optimal nonlinear filtering.

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Lecture 1 - Optimization Techniques - Introduction - Study Hour

Use your name:. Thank you for posting a review! We value your input. Share your review so everyone else can enjoy it too. Your review was sent successfully and is now waiting for our team to publish it. Reviews 0. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases.

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The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective more than three objectives optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.

Pareto-Optimal Front Determination. Metaheuristic Optimization Algorithms.


Evolutionary Strategy Algorithms. Genetic Search Algorithms. Evolution Strategy Algorithms.