Venkateswarlu, C: Stochastic Global Optimization Methods and

"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