Advanced Optimization by Nature-Inspired Algorithms

Chapter 1: Overview of Optimization
Summary
This chapter briefly explains optimization and its basic concepts. Also, examples of the different types of engineering optimization problems are presented in this chapter.
1.1 Optimization
1.2 Examples of engineering optimization problems
1.3 Conclusion

Chapter 2: Introduction to Meta-heuristic and Evolutionary Algorithms
Summary
This chapter begins with a brief review of different independent-problem methods for searching the decision space, describes the components of meta-heuristic and evolutionary algorithms by relating them to engineering optimization problems. Other related topics such as coding meta-heuristic and evolutionary algorithms, dealing with constraints, objective functions, solution strategies, are reviewed. A general algorithm is presented that encompasses most of the steps of all known meta-heuristic and evolutionary algorithms. This generic presentation provides a standard reference with which to compare all the known meta-heuristic and evolutionary algorithms. The chapter closes with the performance evaluation of the meta-heuristic and evolutionary algorithms covered by the book.
2.1 Searching decision space for optima
2.2 Definition of terms related meta-heuristic and evolutionary algorithms
2.3 Foundation of meta-heuristic and evolutionary algorithms
2.4 Classification of meta-heuristic and evolutionary algorithms
2.5 Coding meta-heuristic and evolutionary algorithms in both discrete and continuous domains
2.6 Generating random values
2.7 Dealing with constraints
2.8 Fitness functions
2.9 Selection of decision variables, parameters
2.10 Generating new solutions
2.11 The best solution
2.12 Termination criteria
2.13 General algorithm
2.14 Performance evaluation of meta-heuristic and evolutionary algorithms
2.15 Conclusion

Chapter 3: Pattern Search (PS)
Summary
This chapter explains the pattern search (PS) algorithm, which is classified as a direct search method. The chapter starts with a brief literature review of the development of PS, important modification of the algorithm, and its applications to engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the different steps of the algorithm are described in detail. A pseudo code of the algorithm is presented that serves as an easy and sufficient guideline for coding the algorithm.
3.1 Introduction
3.2 Pattern search (PS) foundation
3.3 Generating initial solution
3.4 Generate trial solutions
3.5 Update mesh size
3.6 Termination criteria
3.7 User-defined parameters of the PS
3.8 Pseudo code of the PS
3.9 Conclusion
3.10 References

Chapter 4: The Genetic Algorithm (GA)
Summary
This chapter describes the genetic algorithm (GA), which is a well-known evolutionary algorithm. The chapter starts with a brief literature review of the GA's development, followed by presentation of the modification that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the different steps of the algorithm are described in detail. A pseudo code of the algorithm is presented that serves as an easy and sufficient guideline for coding the algorithm.
4.1 Introduction
4.2 Mapping natural evolution into genetic algorithm (GA)
4.3 Creating the initial population
4.4 Selection of decision variables, parameters
4.4.1. Proportionate selection
4.4.2. Ranking selection
4.4.3. Tournament selection
4.5 Reproduction
4.6 Population diversity and selective pressure4.7 T
About the Author: Dr. Omid Bozorg-Haddad is a Professor at the department of irrigation and reclamation engineering, University of Tehran, Iran. His teaching and research interests include water resources and environmental systems analysis, planning, and management as well as application of optimization algorithms in water related systems. He has published more than 100 articles in peer reviewed journals and 100 papers in conference proceedings. He has also supervised more than 50 M.Sc. and Ph.D. students.

Prof. Hugo Loaiciga served as the Water Commissioner for the City of Santa Barbara for six years before joining the Department in 1988. He received the 2002 Service to the Profession Award from the American Society of Civil Engineers and the Environmental and Water Resources Institute for his "longstanding contributions to research and technical activities" of the two groups, and he was elected a Fellow of the American Society of Civil Engineers for his "outstanding contributions to the planning, analysis, and operation of water resources engineering" in 2007.