book

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

Name: Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
ISBN: 9781119386995

by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga

October 2017

Intermediate to advanced

304 pages

8h 3m

English

Wiley

Read now

Unlock full access

Cover
Title Page
Preface
About the Authors
List of Figures
1 Overview of Optimization
Summary1.1 Optimization1.2 Examples of the Formulation of Various Engineering Optimization Problems1.3 Conclusion
2 Introduction to Meta‐Heuristic and Evolutionary Algorithms
Summary2.1 Searching the Decision Space for Optimal Solutions2.2 Definition of Terms of Meta‐Heuristic and Evolutionary Algorithms2.3 Principles of Meta‐Heuristic and Evolutionary Algorithms2.4 Classification of Meta‐Heuristic and Evolutionary Algorithms2.5 Meta‐Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains2.6 Generating Random Values of the Decision Variables2.7 Dealing with Constraints2.8 Fitness Function2.9 Selection of Solutions in Each Iteration2.10 Generating New Solutions2.11 The Best Solution in Each Algorithmic Iteration2.12 Termination Criteria2.13 General Algorithm2.14 Performance Evaluation of Meta‐Heuristic and Evolutionary Algorithms2.15 Search Strategies2.16 ConclusionReferences
3 Pattern Search
Summary3.1 Introduction3.2 Pattern Search (PS) Fundamentals3.3 Generating an Initial Solution3.4 Generating Trial Solutions3.5 Updating the Mesh Size3.6 Termination Criteria3.7 User‐Defined Parameters of the PS3.8 Pseudocode of the PS3.9 ConclusionReferences
4 Genetic Algorithm
Summary4.1 Introduction4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution4.3 Creating an Initial Population4.4 Selection of Parents to Create a New Generation4.5 Population Diversity and Selective Pressure4.6 Reproduction4.7 Termination Criteria4.8 User‐Defined Parameters of the GA4.9 Pseudocode of the GA4.10 ConclusionReferences
5 Simulated Annealing
Summary5.1 Introduction5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process5.3 Generating an Initial State5.4 Generating a New State5.5 Acceptance Function5.6 Thermal Equilibrium5.7 Temperature Reduction5.8 Termination Criteria5.9 User‐Defined Parameters of the SA5.10 Pseudocode of the SA5.11 ConclusionReferences

6 Tabu Search
Summary6.1 Introduction6.2 Tabu Search (TS) Foundation6.3 Generating an Initial Searching Point6.4 Neighboring Points6.5 Tabu Lists6.6 Updating the Tabu List6.7 Attributive Memory6.8 Aspiration Criteria6.9 Intensification and Diversification Strategies6.10 Termination Criteria6.11 User‐Defined Parameters of the TS6.12 Pseudocode of the TS6.13 ConclusionReferences
7 Ant Colony Optimization
Summary7.1 Introduction7.2 Mapping Ant Colony Optimization (ACO) to Ants’ Foraging Behavior7.3 Creating an Initial Population7.4 Allocating Pheromone to the Decision Space7.5 Generation of New Solutions7.6 Termination Criteria7.7 User‐Defined Parameters of the ACO7.8 Pseudocode of the ACO7.9 ConclusionReferences
8 Particle Swarm Optimization
Summary8.1 Introduction8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals8.3 Creating an Initial Population of Particles8.4 The Individual and Global Best Positions8.5 Velocities of Particles8.6 Updating the Positions of Particles8.7 Termination Criteria8.8 User‐Defined Parameters of the PSO8.9 Pseudocode of the PSO8.10 ConclusionReferences
9 Differential Evolution
Summary9.1 Introduction9.2 Differential Evolution (DE) Fundamentals9.3 Creating an Initial Population9.4 Generating Trial Solutions9.5 Greedy Criteria9.6 Termination Criteria9.7 User‐Defined Parameters of the DE9.8 Pseudocode of the DE9.9 ConclusionReferences
10 Harmony Search
Summary10.1 Introduction10.2 Inspiration of the Harmony Search (HS)10.3 Initializing the Harmony Memory10.4 Generating New Harmonies (Solutions)10.5 Updating the Harmony Memory10.6 Termination Criteria10.7 User‐Defined Parameters of the HS10.8 Pseudocode of the HS10.9 ConclusionReferences
11 Shuffled Frog‐Leaping Algorithm
Summary11.1 Introduction11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA)11.3 Creating an Initial Population11.4 Classifying Frogs into Memeplexes11.5 Frog Leaping11.6 Shuffling Process11.7 Termination Criteria11.8 User‐Defined Parameters of the SFLA11.9 Pseudocode of the SFLA11.10 ConclusionReferences
12 Honey‐Bee Mating Optimization
Summary12.1 Introduction12.2 Mapping Honey‐Bee Mating Optimization (HBMO) to the Honey‐Bee Colony Structure12.3 Creating an Initial Population12.4 The Queen12.5 Drone Selection12.6 Brood (New Solution) Production12.7 Improving Broods (New Solutions) by Workers12.8 Termination Criteria12.9 User‐Defined Parameters of the HBMO12.10 Pseudocode of the HBMO12.11 ConclusionReferences
13 Invasive Weed Optimization
Summary13.1 Introduction13.2 Mapping Invasive Weed Optimization (IWO) to Weeds’ Biology13.3 Creating an Initial Population13.4 Reproduction13.5 The Spread of Seeds13.6 Eliminating Weeds with Low Fitness13.7 Termination Criteria13.8 User‐Defined Parameters of the IWO13.9 Pseudocode of the IWO13.10 ConclusionReferences
14 Central Force Optimization
Summary14.1 Introduction14.2 Mapping Central Force Optimization (CFO) to Newton’s Gravitational Law14.3 Initializing the Position of Probes14.4 Calculation of Accelerations14.5 Movement of Probes14.6 Modification of Deviated Probes14.7 Termination Criteria14.8 User‐Defined Parameters of the CFO14.9 Pseudocode of the CFO14.10 ConclusionReferences
15 Biogeography‐Based Optimization
Summary15.1 Introduction15.2 Mapping Biogeography‐Based Optimization (BBO) to Biogeography Concepts15.3 Creating an Initial Population15.4 Migration Process15.5 Mutation15.6 Termination Criteria15.7 User‐Defined Parameters of the BBO15.8 Pseudocode of the BBO15.9 ConclusionReferences
16 Firefly Algorithm
Summary16.1 Introduction16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies16.3 Creating an Initial Population16.4 Attractiveness16.5 Distance and Movement16.6 Termination Criteria16.7 User‐Defined Parameters of the FA16.8 Pseudocode of the FA16.9 ConclusionReferences
17 Gravity Search Algorithm
Summary17.1 Introduction17.2 Mapping the Gravity Search Algorithm (GSA) to the Law of Gravity17.3 Creating an Initial Population17.4 Evaluation of Particle Masses17.5 Updating Velocities and Positions17.6 Updating Newton’s Gravitational Factor17.7 Termination Criteria17.8 User‐Defined Parameters of the GSA17.9 Pseudocode of the GSA17.10 ConclusionReferences
18 Bat Algorithm
Summary18.1 Introduction18.2 Mapping the Bat Algorithm (BA) to the Behavior of Microbats18.3 Creating an Initial Population18.4 Movement of Virtual Bats18.5 Local Search and Random Flying18.6 Loudness and Pulse Emission18.7 Termination Criteria18.8 User‐Defined Parameters of the BA18.9 Pseudocode of the BA18.10 ConclusionReferences
19 Plant Propagation Algorithm
Summary19.1 Introduction19.2 Mapping the Natural Process to the Planet Propagation Algorithm (PPA)19.3 Creating an Initial Population of Plants19.4 Normalizing the Fitness Function19.5 Propagation19.6 Elimination of Extra Solutions19.7 Termination Criteria19.8 User‐Defined Parameters of the PPA19.9 Pseudocode of the PPA19.10 ConclusionReferences
20 Water Cycle Algorithm
Summary20.1 Introduction20.2 Mapping the Water Cycle Algorithm (WCA) to the Water Cycle20.3 Creating an Initial Population20.4 Classification of Raindrops20.5 Streams Flowing to the Rivers or Sea20.6 Evaporation20.7 Raining Process20.8 Termination Criteria20.9 User‐Defined Parameters of the WCA20.10 Pseudocode of the WCA20.11 ConclusionReferences
21 Symbiotic Organisms Search
Summary21.1 Introduction21.2 Mapping Symbiotic Relations to the Symbiotic Organisms Search (SOS)21.3 Creating an Initial Ecosystem21.4 Mutualism21.5 Commensalism21.6 Parasitism21.7 Termination Criteria21.8 Pseudocode of the SOS21.9 ConclusionReferences
22 Comprehensive Evolutionary Algorithm
Summary22.1 Introduction22.2 Fundamentals of the Comprehensive Evolutionary Algorithm (CEA)22.3 Generating an Initial Population of Solutions22.4 Selection22.5 Reproduction22.6 Roles of Operators22.7 Input Data to the CEA22.8 Termination Criteria22.9 Pseudocode of the CEA22.10 ConclusionReferences
Wiley Series in Operations Research and Management Science
Index
End User License Agreement

Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

11 Shuffled Frog‐Leaping Algorithm

Summary

This chapter describes the shuffled frog‐leaping algorithm (SFLA), which is a swarm intelligence algorithm based on the memetic evolution of the social behavior of frogs.

11.1 Introduction

The shuffled frog‐leaping algorithm (SFLA) is a swarm intelligence algorithm based on the social behavior of frogs. It was proposed by Eusuff and Lansey (2003). Eusuff et al. (2006) demonstrated the capability of the SFLA for calibrating groundwater models and to design water distribution networks problems. They also compared the results of the SFLA with those of the genetic algorithm (GA). The comparison proved that the SFLA can be an effective tool for solving combinatorial optimization problems. Chung and Lansey (2008) developed a general large‐scale water supply model to minimize the total system cost by integrating a mathematical supply system representation applying the SFLA. The results showed that the SFLA found solutions that satisfied all the constraints for the studied networks. Seifollahi‐Aghmiuni et al. (2011) implemented the SFLA to analyze the efficiency of a designed network based on nodal demand uncertainty during the operational period. Zhao et al. (2011) presented a combined water quality assessment model constructed based on artificial neural network (ANN) and the SFLA, which was applied to train the initialized data from water quality criteria. Balamurugan (2012) applied the SFLA to achieve the optimum solution of economic ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781119386995Purchase book

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design