Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes

Presents various classical as well as stochastic, evolutionary, and artificial intelligence optimization algorithms emphasizing their design, analysis and implementation to solve complex optimization problems of different domains

Ch. Venkateswarlu (Author), Satya Eswari Jujjavarapu (Author)

9780128173923, Elsevier Science

Paperback, published 18 November 2019

310 pages
23.4 x 19 x 2 cm, 0.59 kg

"The book contains a good introduction to optimization algorithms including classical analytical methods, stochastic and evolutionary optimization algorithms and their applications. For algorithms, additionally to their descriptions, advantages and disadvantages are discussed and basic case problems are analyzed. A particular consideration is given to stochastic and evolutionary optimization algorithms because they can successfully overcome difficulties connected with bad differentiability, high dimensionality, multimodality and nonlinearity in objective functions and constraints. Probably the most valuable contribution of the book is application of optimization algorithms to real-life problems which were solved in chemical, biochemical, pharmaceutical and environmental processes. The book can be recommended to graduate students, researchers and practicing engineers. The book consists of ten chapters. In Chapter 1, basic features of optimization are introduced. In Chapters 2, classical analytical methods including optimization with constraints are presented. Chapter 3 contains numerical search methods, e.g., gradient method. In Chapter 4, stochastic and artificial intelligence optimization algorithms are considered: genetic algorithms, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony optimization, cuckoo search algorithm. In Chapter 5, these algorithms are applied to base case problems.In Chapter 6, differential evolution optimization method is applied to control problem in chemistry. In Chapter 7, application of artificial neural network is applied to optimization of biochemical processes. Chapter 8 describes application to multiobjective optimization. In Chapter 9, artificial intelligence optimization algorithms are applied to optimization of environmental processes. Chapter 10 contains conclusions." --ZBMath

Stochastic global optimization methods and applications to chemical, biochemical, pharmaceutical and environmental processes presents various algorithms that include the genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony optimization, and cuckoo search algorithm. The design and analysis of these algorithms is studied by applying them to solve various base case and complex optimization problems concerning chemical, biochemical, pharmaceutical, and environmental engineering processes.

1. Basic Concepts2. Classical Analytical Methods of Optimization3. Numerical Search Methods for Unconstrained Optimization Problems4. Stochastic and Evolutionary Optimization Algorithms5. Application of Stochastic and Evolutionary Optimization Algorithms to Base Case Problems6. Applications to Chemical Processes7. Applications to Biochemical Processes8. Applications to Pharmaceutical Processes9. Applications to Environmental Processes10. Conclusions

Subject Areas: Enterprise software [UFL], Chemical engineering [TDCB], Biochemical engineering [TC]