CONTENTS -- Preface -- Chapter 1 Introduction Gade Pandu Rangaiah -- 1. Optimization in Chemical Engineering -- 2. Examples Requiring Global Optimization -- 2.1. Modified Himmelblau function -- 2.2. Ellipsoid and hyperboloid intersection -- 2.3. Reactor design example -- 2.4. Stepped paraboloid function -- 3. Global Optimization Techniques -- 4. Scope and Organization of the Book -- References -- Exercises -- Chapter 2 Formulation and Illustration of Luus-Jaakola Optimization Procedure Rein Luus -- 1. Introduction -- 2. LJ Optimization Procedure -- 2.1. Example of an optimization problem-diet problem with 7 foods -- 2.2. Example 2-Alkylation process optimization -- 2.3. Example 3 -Gibbs free energy minimization -- 3. Handling Equality Constraints -- 3.1. Example 4 -Geometric problem -- 3.2. Example 5 -Design of columns -- 4. Effect of Parameters -- 4.1. Example 7 -Minimization of Rosenbrock function -- 4.2. Example 8 -Maximization of the Shubert function -- 5. Conclusions -- References -- Exercises -- Chapter 3 Adaptive Random Search and Simulated Annealing Optimizers: Algorithms and Application Issues Jacek M. Je˙zowski, Grzegorz Poplewski and Roman Bochenek -- 1. Introduction and Motivation -- 2. Adaptive Random Search Approach -- 2.1. Introduction -- 3. Simulated Annealing with Simplex Method -- 3.1. Introduction -- 3.2. SA-S/1 algorithm -- 3.3. Important mechanisms of SA-S/1 algorithm -- 3.3.1. Initial simplex generation -- 3.3.2. Determination of the initial temperature -- 3.3.3. Acceptance criterion -- 3.3.4. Cooling scheme-Temperature decrease -- 3.3.5. Equilibrium criterion -- 3.3.6. Stopping (convergence) criterion -- 4. Tests, Control Parameters Settings and Important Application Issues -- 4.1. Tests-Test problems and results -- 4.2. Parameter settings for SA-S/1 algorithm -- 4.2.1. Cooling scheme -- 4.2.2. Influence of parameter INV.

4.2.3. Influence of parameter K in the equilibrium criterion -- 4.2.4. Influence of parameter γ in the adaptive cooling scheme -- 4.2.5. Influence of parameter T min -- 4.3. Results and analysis of tests for LJ-MM algorithm -- 4.4. Selected application issues -- 4.4.1. Dealing with inequality constraints -- 4.4.2. Dealing with equality constraints -- 4.5. Problem size effect -- 5. Summary -- Symbols -- Superscripts -- Acronyms -- References -- Exercises -- Appendix A -- Chapter 4 Genetic Algorithms in Process Engineering: Developments and Implementation Issues Abdunnaser Younes, Ali Elkamel and Shawki Areibi -- 1. Introduction -- 2. Review of Chemical Engineering Applications -- 3. The Basic Genetic Algorithm -- 3.1. Encoding -- 3.2. Fitness evaluation -- 3.3. Initial population -- 3.4. Selection -- 3.4.1. Fitness proportionate selection -- 3.4.2. Other selection schemes -- 3.5. Crossover -- 3.6. Mutation -- 3.7. Theoretical aspects -- 3.8. General characteristics -- 3.8.1. Advantages -- 3.8.2. Disadvantages -- 3.9. When should we use GAs? -- 4. Implementation Issues -- 4.1. Primary decisions -- 4.1.1. Encoding -- 4.2. Complex evaluations -- 4.2.1. Reducing the total number of evaluations -- 4.2.2. Reducing the cost of individual evaluation -- 4.3. Constraint handling -- 4.4. Genetic operators and parameters -- 4.4.1. Genetic operators -- 4.4.2. Parameter tuning -- 4.4.3. Termination criteria -- 5. Advanced Topics -- 5.1. Maintaining population diversity -- 5.1.1. Adaptive parameter setting -- 5.1.2. Island-based genetic algorithms -- 5.2. Hybridization -- 6. Conclusions -- References -- Chapter 5 Tabu Search for Global Optimization of Problems Having Continuous Variables Sim Mong Kai, Gade Pandu Rangaiah and Mekapati Srinivas -- 1. Introduction -- 2. Tabu Search with Quasi-Newton (TS-QN) Method.

3. Application of TS-QN to the Modified Himmelblau Function -- 4. TS Methods for Global Optimization of Continuous Problems -- 4.1. Multi-objective TS (MOTS) -- 4.2. Use of TL in other stochastic methods -- 4.3. Other issues/developments -- 5. TS Software -- 6. Chemical Engineering Applications of TS -- 7. Features of Tabu Search -- 8. Short Term Memory (STM) -- 9. Aspiration Criterion -- 10. Candidate List -- 11. Long Term Memory (LTM) -- 12. Intensification -- 12.1. Diversification -- 12.2. Path relinking -- 13. Conclusions -- References -- Chapter 6 Differential Evolution: Method, Developments and Chemical Engineering Applications Chen Shaoqiang, Gade Pandu Rangaiah and Mekapati Srinivas -- 1. Introduction -- 2. Description of DE -- 3. Developments of DE -- 3.1. Initialization -- 3.2. Mutation operation -- 3.3. Crossover -- 3.4. Selection -- 3.5. Hybrid methods and new generation -- 4. Chemical Engineering Applications -- 5. Conclusions -- References -- 6. Exercises -- Chapter 7 Ant Colony Optimization: Details of Algorithms Suitable for Process Engineering V. K. Jayaraman, P. S. Shelokar, P. Shingade, V. Pote, R. Baskar and B. D. Kulkarni -- 1. Introduction -- 2. ACO for Continuous Function Optimization -- 3. Continuous Ant Colony Optimization Algorithm (CACO) -- 3.1. Local search -- 3.2. Global search -- 3.3. Pheromone evaporation -- 3.4. Steps in CACO algorithm -- 4. ACO Algorithm for Combinatorial Optimization -- 5. Scheduling of Serial Multiproduct Batch Plant -- 5.1. UIS policy -- 5.2. ZW policy -- 6. ACO Algorithm for Scheduling of SerialMultiproduct Batch Plant -- 7. ACO for Multiobjective Scheduling of SerialMultiproduct Batch Plant -- 7.1. Multiobjective optimization (MOO) problem definition -- 8. ACO for Knowledge Discovery in Process Data -- 9. ACO for Data Clustering -- 10. ACO Algorithm for Rule Based Classification.

11. Results and Discussion -- 11.1. CACO for unconstrained continuous functions -- 12. CACO for Constrained Continuous Functions -- 12.1. Scheduling of batch processes -- 12.2. ACO for data clustering -- 13. Conclusions -- Abbreviations -- References -- Acknowledgement -- Chapter 8 Particle Swarm Optimization for Solving NLP and MINLP in Chemical Engineering Bassem Jarboui, Houda Derbel, Mansour Eddaly and Patrick Siarry -- 1. Introduction -- 2. The Initial Version of the PSO Algorithm -- 3. Simple Modifications to the Original PSO -- 3.1. Maximum velocity -- 3.2. Inertia weight -- 3.3. Constriction factor -- 3.4. Stopping criteria -- 4. Neighborhood Selection Strategies -- 4.1. Global best / Local best PSO -- 5. Extensions of Basic PSO -- 6. Multi-Start PSO Algorithms -- 7. Gravity Center Technique -- 8. Application of PSO to Mixed-Integer Nonlinear Programming (MINLP) -- 8.1. Applications to constrained problems -- 8.2. Applications in chemical engineering -- 9. Application of PSO to the NLP and MINLP Problems -- 9.1. Initialization -- 9.2. Handling discrete variables and bound checking -- 9.3. Handling constraints -- 10. Numerical Results -- 11. Conclusion and Future Directions -- References -- Chapter 9 An Introduction to the Harmony Search Algorithm Gordon Ingram and Tonghua Zhang -- 1. Introduction -- 2. Applications of the Harmony Search -- 2.1. Selected applications broadly related to Chemical Engineering -- 2.2. Chemical Engineering applications -- 3. Basic Harmony Search Algorithm -- 3.1. Basic harmony search for continuous decision variables -- Specifying the algorithm parameters -- Initializing harmony memory -- Improvising a new harmony -- Updating harmony memory -- Termination criterion -- 3.2. The harmony search approach compared with other global optimization methods.

3.3. Basic harmony search for discrete and mixed variable problems -- 3.4. Stochastic partial derivative for discrete variables -- 3.5. Selection of algorithm parameter values -- 3.6. Handling equality and inequality constraints -- 3.7. Performance of the harmony search compared to other SGO methods -- 4. Variations on the Basic Harmony Search Algorithm -- 4.1. Minor variations on the basic harmony search -- Harmony memory initialization -- Sorting harmony memory -- Termination criteria -- 4.2. Techniques involving parameter adaptation -- Variable HMCR and PAR -- Variable PAR and bandwidth -- Tightening of variable bounds -- 4.3. Techniques that modify the HS operators -- Biased selection -- Pitch adjusting replaced by global best solution -- Pitch adjusting replaced by mutation -- Biased pitch adjusting -- Random playing replaced by simulated annealing -- Ensemble consideration for new harmonies -- Sequential Quadratic Programming (SQP) refinement of new harmonies -- Special training of elite harmonies -- Generation of multiple harmonies -- Chaotic harmonies -- Prevention of overlapping harmonies -- 4.4. Specialized applications and hybrid algorithms -- 4.5. Reflection on the HS variations -- 5. Harmony Search Software -- 6. Illustrative Example -- 7. Conclusions and Opportunities for Further Research -- Acknowledgment -- References -- Exercises -- Chapter 10 Meta-Heuristics: Evaluation and Reporting Techniques Abdunnaser Younes, Ali Elkamel and Shawki Areibi -- 1. Introduction -- 2. General Considerations -- 2.1. Performance measures -- 2.1.1. Solution quality -- 2.1.2. Algorithm efficiency -- 2.1.3. Quality-efficiency trade-off -- 2.2. Algorithm robustness -- 2.3. Effects of stochastic sampling -- 2.4. Parameter tuning -- 3. Test Problems -- 3.1. Classification of test problems -- 3.2. Selecting the test suite.

4. Experiment Design and Parameter Settings.