Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems.

This book examines the latest developments of metaheuristics and their applications in water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence.

Key Features

  • Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation
  • Develops new hybrid and advanced methods suitable for civil engineering problems at all levels
  • Appropriate for researchers and advanced students to help to develop their work


Academic researchers and lecturers in civil engineering and computer sciences as well as industrial practitioners.

Table of Contents

List of contributors

1. Optimization and Metaheuristic Algorithms in Engineering

1.1 Introduction

1.2 Three Issues in Optimization

1.3 Metaheuristics

1.4 Artificial Neural Networks

1.5 Genetic Programming


PART ONE: Water Resources

2. A Review on Application of Soft Computing Methods in Water Resources Engineering

2.1 Introduction

2.2 Soft Computing Techniques

2.3 Implementation of Soft Computing Techniques

2.4 Conclusion



3. Genetic Algorithms and Their Applications to Water Resources Systems

3.1 Introduction

3.2 Genetic Algorithms

3.3 Review of GA Applications to Water Resource Problems

3.4 The GA Process for a Reservoir Operation Problem

3.5 Conclusions


4. Application of the Hybrid HS–Solver Algorithm to the Solution of Groundwater Management Problems

4.1 Introduction

4.2 Development of the Hybrid HS–Solver Algorithm

4.3 Formulation of the Management Problem

4.4 Numerical Applications

4.5 Conclusions



5. Water Distribution Networks Designing by the Multiobjective Genetic Algorithm and Game Theory

5.1 Introduction

5.2 The Objectives of WDN Optimization

5.3 The Hydraulic of WDN

5.4 Basic Concepts: GA, Multiobjective Optimization, and Game Theory

5.5 Methodology

5.6 Case Study

5.7 The Biobjective Optimization Problem



6. Ant Colony Optimization for Estimating Parameters of Flood Frequency Distributions

6.1 Introduction

6.2 A Review of Previous Work

6.3 Standard ACO

6.4 Improved ACO

6.5 Other Well-Known Methods of Parameter Estimation

6.6 Frequency Distributions

6.7 Simulation a


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About the authors

Xin-She Yang

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).