**Meet the editor**

Dr. Olympia Roeva received the M.Sc. degree (1998) and Ph.D. degree (2007) from the Technical University – Sofia, Bulgaria. At present she is an associate professor in Department of Bioinformatics and Mathematical Modeling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. Dr. Roeva is one of the scientists playing a significant role in the renovation of

International Journal Bioautomation. She has received various scientific awards, including a Diploma from Union of Scientists in Bulgaria for High Scientific Achievements 2008. Her research interests are in the fields of modeling, optimization and control of cultivation processes, functional state modeling approach, metaheuristic algorithms and generalized nets.

Contents

**Preface IX** 

Chapter 1 **Different Tools on Multi-Objective** 

Nor Aishah Saidina Amin and I. Istadi

Chapter 3 **Evolutionary Multi-Objective Algorithms 53** 

Eunice Ponce de León and Elva Díaz

Chapter 4 **Evolutionary Algorithms Based** 

Chapter 6 **A Hybrid Parallel Genetic Algorithm** 

Chapter 7 **Hybrid Genetic Algorithm-Support** 

**for Reliability Optimization 127**  Ki Tae Kim and Geonwook Jeon

**Vector Machine Technique for Power Tracing in Deregulated Power Systems 147**  Mohd Wazir Mustafa, Mohd Herwan Sulaiman, Saifulnizam Abd. Khalid and Hussain Shareef

Elias D. Niño

Chapter 5 **Evolutionary Techniques** 

**and Neural Networks for Optimal Design of Fractal Frequency Selective Surfaces 27** 

Aurora Torres, Dolores Torres, Sergio Enriquez,

**in Multi-Objective Optimization Problems in Non-Standardized Production Processes 109**  Mariano Frutos, Ana C. Olivera and Fernando Tohmé

**on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems 81** 

Chapter 2 **Application of Bio-Inspired Algorithms** 

**Optimization of a Hybrid Artificial Neural Network –** 

Paulo Henrique da Fonseca Silva, Marcelo Ribeiro da Silva, Clarissa de Lucena Nóbrega and Adaildo Gomes D'Assunção

**Genetic Algorithm for Plasma Chemical Reactor Modelling 1** 

## Contents

### **Preface** XI


Saifulnizam Abd. Khalid and Hussain Shareef

X Contents


## Preface

*Genetic Algorithms* are a part of *Evolutionary Computing*, which is a rapidly growing area of *Artificial Intelligence*. The popularity of *Genetic Algorithms* is reflected in the increasing amount of literature devoted to theoretical works and real-world applications in both scientific and engineering areas. The useful application and the proper combination of the different *Genetic Algorithms* with the various optimization algorithms is still an open research topic.

This book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multiobjective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. Hybridization of *Genetic Algorithms* is getting popular due to their capabilities in handling different problems involving complexity, noisy environment, uncertainty, etc. The book presents hybrid techniques based on *Artificial Neural Network*, *Fuzzy Sets*, *Automata Theory*, *other metaheuristic* or *classical algorithms*, etc. The volume examines various examples of algorithms in different real-world application domains as *graph growing problem*, *speech synthesis*, *traveling salesman problem*, *scheduling problems*, *antenna design*, *genes design*, *modeling of chemical and biochemical processes* etc.

The book, organized in 17 chapters, begins with several applications of *Hybrid Genetic Algorithms* in wide range of problems. Further, some applications of *Genetic Algorithms* and other heuristic search methods are presented.

The objective of *Chapter 1* is to model and to optimize the process performances simultaneously in the plasma-catalytic conversion of methane such that the optimal process performances are obtained at the given process parameters. A *Hybrid Artificial Neural Network-Genetic Algorithm* (*ANN-GA*) is successfully developed to model, to simulate and to optimize simultaneously a catalytic-dielectric-barrier discharge plasma reactor. The integrated *ANN-GA* method facilitates powerful modeling and multi-objectives optimization for co-generation of synthesis gas, C2 and higher hydrocarbons from methane and carbon dioxide in a dielectric barrier discharge plasma reactor.

#### XII Preface

*Chapter 2* presents a new fast and accurate electromagnetic optimization technique combining full-wave method of moments, bio-inspired algorithms, continuous *Genetic Algorithm* and *Particle Swarm Optimization*, and multilayer perceptrons *Artificial Neural Networks*. The proposed optimization technique is applied for optimal design of frequency selective surfaces with fractal patch elements. A fixed frequency selective surface screen geometry is chosen a priori and then a smaller subset of frequency selective surface design variables is optimized to achieve a desired bandstop filter specification.

Preface XI

*SVM*) adopts real and reactive power tracing output determined by Superposition method as an estimator to train the model. The results show that *GA-SVM* gives good accuracy in predicting the generators' output and compared well with Superposition

*Chapter 8* provides an insight into the general reasoning behind selection of the *Genetic Algorithms* control parameters, discuss the ways of boosting the algorithm efficiency, and finally introduce a simple *Global-local Hybrid Genetic Algorithms* capable of fast and reliable optimization of multi-parameter and multi-extremum functions. The effectiveness of the proposed algorithm is demonstrated by numerical examples, namely: synthesis of linear antenna arrays with pencil-beam and flat-top patterns.

*Chapter 9* introduces a hybrid methodology, the *Heuristics Backtracking*, an approach that combines a search algorithm, the backtracking, integer linear programming and *Genetic Algorithms* to solve the three dimensional knapsack loading problem considering weight distribution. The authors show that the *Heuristics Backtracking* achieved good results without the commonly great trade-off between the utilization of container and a good weight distribution. Some benchmark tests taken from literature are used to validate the performance and efficiency of the *Heuristics Backtracking*

*Chapter 10* introduces two *Hybrid Genetic Algorithms* to solve the sequence-dependent setup times single machine problem. The proposed approaches are essentially based on adapting highly specialized genetic operators to the specificities of the studied problem. The numerical experiments demonstrate the efficiency of the hybrid algorithms for this problem. A natural conclusion from these experimental results is that *Genetic Algorithms* may be robust and efficient alternative to solve this problem.

Chapter 11 presents the Group Method of Data Handling-type Neural Network with Genetic Algorithm used to develop the early egg production in *broiler breeder*. By means of the Group Method of Data Handling Algorithm, a model can be represented as a set of quadratic polynomials. Genetic Algorithms are deployed to assign the number of neurons (polynomial equations) in the network and to find the optimal set

*Chapter 12* discusses some of the computational issues for evolutionary searches to find gene-regulatory sequences. Here the *retroGenetic Algorithm* technique is introduced. Proposed *Genetic Algorithm* crossover operator is inspired by retroviral recombination and in vitro DNA shuffling mechanisms to copy blocks of genetic information. The authors present particular results on the efficiency of *retroGenetic Algorithm* in

*Chapter 13* examines the use of *Genetic Algorithms* and *Ant Colony Optimization* for parameter identification of a system of nonlinear differential equations modeling the fed-batch cultivation process of the bacteria *E. coli*. The results from both

methodology as well as its applicability to cutting-stock problems.

of appropriate coefficients of the quadratic expressions.

comparison with the standard *Genetic Algorithm*.

method and load flow study.

The main contribution of the *Chapter 3* is the test of the *Hybrid MOEA-HCEDA Algorithm* and the quality index based on the Pareto front used in the graph drawing problem. The Pareto front quality index printed on each generation of the algorithm showed a convergent curve. The results of the experiments show that the algorithm converges. A graphical user interface is constructed providing users with a tool for a friendly and easy to use graphs display. The automatic drawing of optimized graphs makes it easier for the user to compare results appearing in separate windows, giving the user the opportunity to choose the graph design which best suits their needs.

*Chapter 4* studies metaheuristics based on the *Automata Theory* for the multi-objective optimization of combinatorial problems. The *SAMODS* (*Simulated Annealing inspired Algorithm*), *SAGAMODS* (*Evolutionary inspired Algorithm*) and *EMODS* (using *Tabu Search*) algorithms are presented. Presented experimental results of each proposed algorithm using multi-objective metrics from the specialized literature show that the *EMODS* has the best performance. In some cases the behavior of *SAMOD*S and *SAGAMODS* tend to be the same – similar error rate.

*Chapter 5* presents a *Hybrid Genetic Algorithm* (*Genetic Algorithm* linked to a *Simulated Annealing*) intended to solve the Flexible Job-Shop Scheduling Problem procedure able to schedule the production in a Job-Shop manufacturing system. The authors show that this *Hybrid Genetic Algorithm* yields more solutions in the Approximate Pareto Frontier than other algorithms. A platform and programming language independent interface for search algorithms has been used as a guide for the implementation of the proposed hybrid algorithm.

*Chapter 6* suggests mathematical programming models and a *Hybrid Parallel Genetic Algorithm* (*HPGA*) for reliability optimization with resource constraints. The considered algorithm includes different heuristics such as swap, 2-opt, and interchange for an improvement solution. The experimental results of *HPGA* are compared with the results of existing meta-heuristics. The suggested algorithm presents superior solutions to all problems and found that the performance is superior to existing meta-heuristics.

*Chapter 7* discusses the effectiveness of *Genetic Algorithms* in determining the optimal values of hyper-parameters of Least Squares-Support Vector Machines to solve power tracing problem. The developed hybrid *Genetic Algorithm-Support Vector Machines* (*GA-*

*SVM*) adopts real and reactive power tracing output determined by Superposition method as an estimator to train the model. The results show that *GA-SVM* gives good accuracy in predicting the generators' output and compared well with Superposition method and load flow study.

X Preface

specification.

*Chapter 2* presents a new fast and accurate electromagnetic optimization technique combining full-wave method of moments, bio-inspired algorithms, continuous *Genetic Algorithm* and *Particle Swarm Optimization*, and multilayer perceptrons *Artificial Neural Networks*. The proposed optimization technique is applied for optimal design of frequency selective surfaces with fractal patch elements. A fixed frequency selective surface screen geometry is chosen a priori and then a smaller subset of frequency selective surface design variables is optimized to achieve a desired bandstop filter

The main contribution of the *Chapter 3* is the test of the *Hybrid MOEA-HCEDA Algorithm* and the quality index based on the Pareto front used in the graph drawing problem. The Pareto front quality index printed on each generation of the algorithm showed a convergent curve. The results of the experiments show that the algorithm converges. A graphical user interface is constructed providing users with a tool for a friendly and easy to use graphs display. The automatic drawing of optimized graphs makes it easier for the user to compare results appearing in separate windows, giving the user the opportunity to choose the graph design which best suits their needs.

*Chapter 4* studies metaheuristics based on the *Automata Theory* for the multi-objective optimization of combinatorial problems. The *SAMODS* (*Simulated Annealing inspired Algorithm*), *SAGAMODS* (*Evolutionary inspired Algorithm*) and *EMODS* (using *Tabu Search*) algorithms are presented. Presented experimental results of each proposed algorithm using multi-objective metrics from the specialized literature show that the *EMODS* has the best performance. In some cases the behavior of *SAMOD*S and

*Chapter 5* presents a *Hybrid Genetic Algorithm* (*Genetic Algorithm* linked to a *Simulated Annealing*) intended to solve the Flexible Job-Shop Scheduling Problem procedure able to schedule the production in a Job-Shop manufacturing system. The authors show that this *Hybrid Genetic Algorithm* yields more solutions in the Approximate Pareto Frontier than other algorithms. A platform and programming language independent interface for search algorithms has been used as a guide for the implementation of the

*Chapter 6* suggests mathematical programming models and a *Hybrid Parallel Genetic Algorithm* (*HPGA*) for reliability optimization with resource constraints. The considered algorithm includes different heuristics such as swap, 2-opt, and interchange for an improvement solution. The experimental results of *HPGA* are compared with the results of existing meta-heuristics. The suggested algorithm presents superior solutions to all problems and found that the performance is superior

*Chapter 7* discusses the effectiveness of *Genetic Algorithms* in determining the optimal values of hyper-parameters of Least Squares-Support Vector Machines to solve power tracing problem. The developed hybrid *Genetic Algorithm-Support Vector Machines* (*GA-*

*SAGAMODS* tend to be the same – similar error rate.

proposed hybrid algorithm.

to existing meta-heuristics.

*Chapter 8* provides an insight into the general reasoning behind selection of the *Genetic Algorithms* control parameters, discuss the ways of boosting the algorithm efficiency, and finally introduce a simple *Global-local Hybrid Genetic Algorithms* capable of fast and reliable optimization of multi-parameter and multi-extremum functions. The effectiveness of the proposed algorithm is demonstrated by numerical examples, namely: synthesis of linear antenna arrays with pencil-beam and flat-top patterns.

*Chapter 9* introduces a hybrid methodology, the *Heuristics Backtracking*, an approach that combines a search algorithm, the backtracking, integer linear programming and *Genetic Algorithms* to solve the three dimensional knapsack loading problem considering weight distribution. The authors show that the *Heuristics Backtracking* achieved good results without the commonly great trade-off between the utilization of container and a good weight distribution. Some benchmark tests taken from literature are used to validate the performance and efficiency of the *Heuristics Backtracking* methodology as well as its applicability to cutting-stock problems.

*Chapter 10* introduces two *Hybrid Genetic Algorithms* to solve the sequence-dependent setup times single machine problem. The proposed approaches are essentially based on adapting highly specialized genetic operators to the specificities of the studied problem. The numerical experiments demonstrate the efficiency of the hybrid algorithms for this problem. A natural conclusion from these experimental results is that *Genetic Algorithms* may be robust and efficient alternative to solve this problem.

Chapter 11 presents the Group Method of Data Handling-type Neural Network with Genetic Algorithm used to develop the early egg production in *broiler breeder*. By means of the Group Method of Data Handling Algorithm, a model can be represented as a set of quadratic polynomials. Genetic Algorithms are deployed to assign the number of neurons (polynomial equations) in the network and to find the optimal set of appropriate coefficients of the quadratic expressions.

*Chapter 12* discusses some of the computational issues for evolutionary searches to find gene-regulatory sequences. Here the *retroGenetic Algorithm* technique is introduced. Proposed *Genetic Algorithm* crossover operator is inspired by retroviral recombination and in vitro DNA shuffling mechanisms to copy blocks of genetic information. The authors present particular results on the efficiency of *retroGenetic Algorithm* in comparison with the standard *Genetic Algorithm*.

*Chapter 13* examines the use of *Genetic Algorithms* and *Ant Colony Optimization* for parameter identification of a system of nonlinear differential equations modeling the fed-batch cultivation process of the bacteria *E. coli*. The results from both metaheuristics *Genetic Algorithms* and *Ant Colony Optimization* are compared using the modified Hausdorff distance metric, in place of most common used – least squares regression. Analyzing of average results authors conclude that the *Ant Colony Optimization* algorithm performs better for the considered problem.

*Chapter 14* presents a brief description about the estimation problem of a formant synthesizer, such as the Klatt. The combination of its input parameters to the imitation of human voice is not a simple task, because a reasonable number of parameters have to be combined and each of them has an interval of acceptable values that must be carefully adjusted to produce a specific voice. The authors conclude that it is necessary to develop a more efficient mechanism for evaluating the quality of the generated voice as a whole, and include it in the *Genetic Algorithm speech framework*.

*Chapter 15* discusses about how *Genetic Algorithm* and heuristic search can solve the scheduling problem. As a case study the *"Universitas Pelita Harapan"* timetable is considered. The authors propose the architecture design of the system and show some experiments implementing the system.

The objective of *Chapter 16* is to show a set of single-objective and *multi-objective Genetic Algorithms*, designed by the Optical Communications Group at the University of Valladolid, to optimize the performance of semi-static *Wavelength-Routed Optical Networks* (*WRONs*). The fundamentals of those algorithms, i.e., the chromosome structures, their translation, the optimization goals and the genetic operators employed are described. Moreover, a number of simulation results are also included to show the efficiency of *Genetic Algorithms* when designing *WRONs*.

Finally, *Chapter 17* gives an overview of existing *surrogate* modeling techniques and issues about how to use them for optimization. *Surrogate* modeling techniques are of particular interest for engineering design when high-fidelity, thus expensive analysis codes (e.g. computation fluid dynamics and computational structural dynamics) are used.

The book is designed to be of interest to a wide spectrum of readers. The authors hope that the readers will find this book useful and inspiring.

> **Olympia Roeva**  Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Sofia, Bulgaria
