Showing papers on "Evolutionary programming published in 2009"

Optimization of Power System Operation

Abstract: Preface. 1 Introduction. 1.1 Conventional Methods. 1.2 Intelligent Search Methods. 1.3 Application of Fuzzy Set Theory. 2 Power Flow Analysis. 2.1 Mathematical Model of Power Flow. 2.2 Newton-Raphson Method. 2.3 Gauss-Seidel Method. 2.4 P-Q decoupling Method. 2.5 DC Power Flow. 3 Sensitivity Calculation. 3.1 Introduction. 3.2 Loss Sensitivity Calculation. 3.3 Calculation of Constrained Shift Sensitivity Factors. 3.4 Perturbation Method for Sensitivity Analysis. 3.5 Voltage Sensitivity Analysis. 3.6 Real-Time Application of Sensitivity Factors. 3.7 Simulation Results. 3.8 Conclusion. 4 Classic Economic Dispatch. 4.1 Introduction. 4.2 Input-Output Characteristic of Generator Units. 4.3 Thermal System Economic Dispatch Neglecting Network Losses. 4.4 Calculation of Incremental Power Losses. 4.5 Thermal System Economic Dispatch with Network Losses. 4.6 Hydrothermal System Economic Dispatch. 4.7 Economic Dispatch by Gradient Method. 4.8 Classic Economic Dispatch by Genetic Algorithm. 4.9 Classic Economic Dispatch by Hopfi eld Neural Network. 5 Security-Constrained Economic Dispatch. 5.1 Introduction. 5.2 Linear Programming Method. 5.3 Quadratic Programming Method. 5.4 Network Flow Programming Method. 5.5 Nonlinear Convex Network Flow Programming Method. 5.6 Two-Stage Economic Dispatch Approach. 5.7 Security-Constrained ED by Genetic Algorithms. 6 Multiarea System Economic Dispatch. 6.1 Introduction. 6.2 Economy of Multiarea Interconnection. 6.3 Wheeling. 6.4 Multiarea Wheeling. 6.5 MAED Solved by Nonlinear Convex Network Flow Programming. 6.6 Nonlinear Optimization Neural Network Approach. 6.7 Total Transfer Capability Computation in Multiareas. 7 Unit Commitment. 7.1 Introduction. 7.2 Priority Method. 7.3 Dynamic Programming Method. 7.4 Lagrange Relaxation Method. 7.5 Evolutionary Programming-Based Tabu Search Method. 7.6 Particle Swarm Optimization for Unit Commitment. 7.7 Analytic Hierarchy Process. 8 Optimal Power Flow. 8.1 Introduction. 8.2 Newton Method. 8.3 Gradient Method. 8.4 Linear Programming OPF. 8.5 Modifi ed Interior Point OPF. 8.6 OPF with Phase Shifter. 8.7 Multiple-Objectives OPF. 8.8 Particle Swarm Optimization for OPF. 9 Steady-State Security Regions. 9.1 Introduction. 9.2 Security Corridors. 9.3 Traditional Expansion Method. 9.4 Enhanced Expansion Method. 9.5 Fuzzy Set and Linear Programming. 10 Reactive Power Optimization. 10.1 Introduction. 10.2 Classic Method for Reactive Power Dispatch. 10.3 Linear Programming Method of VAR Optimization. 10.4 Interior Point Method for VAR Optimization Problem. 10.5 NLONN Approach. 10.6 VAR Optimization by Evolutionary Algorithm. 10.7 VAR Optimization by Particle Swarm Optimization Algorithm. 10.8 Reactive Power Pricing Calculation. 11 Optimal Load Shedding. 11.1 Introduction. 11.2 Conventional Load Shedding. 11.3 Intelligent Load Shedding. 11.4 Formulation of Optimal Load Shedding. 11.5 Optimal Load Shedding with Network Constraints. 11.6 Optimal Load Shedding without Network Constraints. 11.7 Distributed Interruptible Load Shedding. 11.8 Undervoltage Load Shedding. 11.9 Congestion Management. 12 Optimal Reconfi guration of Electrical Distribution Network. 12.1 Introduction. 12.2 Mathematical Model of DNRC. 12.3 Heuristic Methods. 12.4 Rule-Based Comprehensive Approach. 12.5 Mixed-Integer Linear Programming Approach. 12.6 Application of GA to DNRC. 12.7 Multiobjective Evolution Programming to DNRC. 12.8 Genetic Algorithm Based on Matroid Theory. 13 Uncertainty Analysis in Power Systems. 13.1 Introduction. 13.2 Defi nition of Uncertainty. 13.3 Uncertainty Load Analysis. 13.4 Uncertainty Power Flow Analysis. 13.5 Economic Dispatch with Uncertainties. 13.6 Hydrothermal System Operation with Uncertainty. 13.7 Unit Commitment with Uncertainties. 13.8 VAR Optimization with Uncertain Reactive Load. 13.9 Probabilistic Optimal Power Flow. 13.10 Comparison of Deterministic and Probabilistic Methods. Author Biography. Index.

Evolutionary Dynamics in Set Structured Populations

Abstract: Evolutionary dynamics are strongly affected by population structure. The outcome of an evolutionary process in a well-mixed population can be very different from that in a structured population. We introduce a powerful method to study dynamical population structure: evolutionary set theory. The individuals of a population are distributed over sets. Individuals interact with others who are in the same set. Any 2 individuals can have several sets in common. Some sets can be empty, whereas others have many members. Interactions occur in terms of an evolutionary game. The payoff of the game is interpreted as fitness. Both the strategy and the set memberships change under evolutionary updating. Therefore, the population structure itself is a consequence of evolutionary dynamics. We construct a general mathematical approach for studying any evolutionary game in set structured populations. As a particular example, we study the evolution of cooperation and derive precise conditions for cooperators to be selected over defectors.

Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique

Abstract: A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive performance with respect to some other state-of-the-art approaches in constrained evolutionary optimization.

Particle Swarm Optimization Methods, Taxonomy and Applications

Abstract: The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1990s and since then, it has been utilized as an optimization tool in various applications, ranging from biological and medical applications to computer graphics and music composition. In this paper, following a brief introduction to the PSO algorithm, the chronology of its evolution is presented and all major PSO-based methods are comprehensively surveyed. Next, these methods are studied separately and their important factors and parameters are summarized in a comparative table. In addition, a new taxonomy of PSO-based methods is presented. It is the purpose of this paper is to present an overview of previous and present conditions of the PSO algorithm as well as its opportunities and challenges. Accordingly, the history, various methods, and taxonomy of this algorithm are discussed and its different applications together with an analysis of these applications are evaluated. among agents on survival of the fittest. Algorithms related to this group include Evolutionary Programming (EP), Genetic Programming (GP), and Differential Evolutionary (DE). The Ontogeny group is associated with the algorithms in which the adaptation of a special organism to its environment is happened. The algorithms like PSO and Genetic Algorithms (GA) are of this type and in fact, they have a cooperative nature in comparison with other types (16). The advantages of above-mention ed categories can be noted as their ability to be developed for various applications and not needing the previous knowledge of the problem space. Their drawbacks include no guarantee in finding an optimum solution and high computational costs in completing Fitness Function (F.F.) in intensive iterations. Among the aforementioned paradigms, the PSO algorithm seems to be an attractive one to study since it has a simple but efficient nature added to being novel. It can even be a substitution for other basic and important evolutionary algorithms. The most important similarity between these paradigms and the GA is in having the seam interactive population. This algorithm, compared to GA, has a faster speed in finding the solutions close to the optimum and it is faster than GA in premature convergence (4).

Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored

Abstract: This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability, and alternative metaheuristics. This discussion motivates some further topics which, from the author’s perspective, constitute good potential areas for future research, namely, constraint-handling techniques, incorporation of user’s preferences and parameter control. This information is expected to be useful for those interested in pursuing research in this area.

Constraint-Handling in Evolutionary Optimization

Abstract: This book is the result of a successful special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches. The book covers six main topics: The first two chapters refer to swarm- intelligence-based approaches. Differential evolution, a very competitive evolutionary algorithm for constrained optimization, is studied in the next three chapters. Two different constraint-handling techniques for evolutionary multiobjective optimization are presented in the two subsequent chapters. Two hybrid approaches, one with a combination of two nature-inspired heuristics and the other with the mix of a genetic algorithm and a local search operator, are detailed in the next two chapters. Finally, a constraint-handling technique designed for a real-world problem and a survey on artificial immune system in constrained optimization are the subjects of the final two chapters. The intended audience for this book comprises graduate students, practitioners and researchers interested on alternative techniques to solve numerical optimization problems in presence of constraints.

Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity

Abstract: The bootstrap problem is often recognized as one of the main challenges of evolutionary robotics: if all individuals from the first randomly generated population perform equally poorly, the evolutionary process won't generate any interesting solution. To overcome this lack of fitness gradient, we propose to efficiently explore behaviors until the evolutionary process finds an individual with a non-minimal fitness. To that aim, we introduce an original diversity-preservation mechanism, called behavioral diversity, that relies on a distance between behaviors (instead of genotypes or phenotypes) and multi-objective evolutionary optimization. This approach has been successfully tested and compared to a recently published incremental evolution method (multi-subgoal evolution) on the evolution of a neuro-controller for a light-seeking mobile robot. Results obtained with these two approaches are qualitatively similar although the introduced one is less directed than multi-subgoal evolution.

A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making

Abstract: This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative.

Dynamic Evolutionary Algorithm With Variable Relocation

Abstract: Many real-world optimization problems have to be solved under the presence of uncertainties. A significant number of these uncertainty problems falls into the dynamic optimization category in which the fitness function varies through time. For this class of problems, an evolutionary algorithm is expected to perform satisfactorily in spite of different degrees and frequencies of change in the fitness landscape. In addition, the dynamic evolutionary algorithm should warrant an acceptable performance improvement to justify the additional computational cost. Effective reuse of previous evolutionary information is a must as it facilitates a faster convergence after a change has occurred. This paper proposes a new dynamic evolutionary algorithm that uses variable relocation to adapt already converged or currently evolving individuals to the new environmental condition. The proposed algorithm relocates those individuals based on their change in function value due to the change in the environment and the average sensitivities of their decision variables to the corresponding change in the objective space. The relocation occurs during the transient stage of the evolutionary process, and the algorithm reuses as much information as possible from the previous evolutionary history. As a result, the algorithm shows improved adaptation and convergence. The newly adapted population is shown to be fitter to the new environment than the original or most randomly generated population. The algorithm has been tested by several dynamic benchmark problems and has shown competitive results compared to some chosen state-of-the-art dynamic evolutionary approaches.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

Abstract: The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

State-of-the Art Review—Evolutionary Algorithms for Vehicle Routing

Abstract: This paper is a survey of the literature on applications of evolutionary algorithms for vehicle routing problems. It reports on genetic algorithms, evolution strategies, and particle swarm optimization when applied to the classical capacitated vehicle routing problem and many of its variants. The performance of evolutionary algorithms is also compared with the best alternative problem-solving approaches on benchmark instances.

Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms

Abstract: Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One prominent example is the fast EP (FEP) algorithm which employs a mutation operator based on the Cauchy distribution instead of the commonly used Gaussian distribution. In this paper, we seek to improve the performance of EP via exploring another important factor of EP, namely, the selection strategy. Three selection rules R1-R3 have been presented to encourage both fitness diversity and solution diversity. Meanwhile, two solution exchange rules R4 and R5 have been introduced to further exploit the preserved genetic diversity. Simple theoretical analysis suggests that through the proper use of R1-R5, EP is more likely to find high-fitness solutions quickly. Our claim has been examined on 25 benchmark functions. Empirical evidence shows that our solution selection and exchange rules can significantly enhance the performance of EP.

Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Abstract: Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. Recently, a lot of emphasis have been laid on hybridizing evolutionary algorithms with MCDM and mathematical programming algorithms to yield a computationally efficient and convergent procedure. In this paper, we test an augmented local search based EMO procedure rigorously on a test suite of constrained and unconstrained multi-objective optimization problems. The success of our approach on most of the test problems not only provides confidence but also stresses the importance of hybrid evolutionary algorithms in solving multi-objective optimization problems.

Applications of artificial intelligence and data mining techniques in soil modeling

Abstract: In recent years, several computer-aided pattern recognition and data mining techniques have been developed for modeling of soil behavior. The main idea behind a pattern recognition system is that it learns adaptively from experience and is able to provide predictions for new cases. Artificial neural networks are the most widely used pattern recognition methods that have been utilized to model soil behavior. Recently, the authors have pioneered the application of genetic programming (GP) and evolutionary polynomial regression (EPR) techniques for modeling of soils and a number of other geotechnical applications. The paper reviews applications of pattern recognition and data mining systems in geotechnical engineering with particular reference to constitutive modeling of soils. It covers applications of artificial neural network, genetic programming and evolutionary programming approaches for soil modeling. It is suggested that these systems could be developed as efficient tools for modeling of soils and analysis of geotechnical engineering problems, especially for cases where the behavior is too complex and conventional models are unable to effectively describe various aspects of the behavior. It is also recognized that these techniques are complementary to conventional soil models rather than a substitute to them.

Investigation on evolutionary optimization of chaos control

Abstract: This work deals with an investigation on optimization of the feedback control of chaos based on the use of evolutionary algorithms. The main objective is to show that evolutionary algorithms are capable of optimization of chaos control. As models of deterministic chaotic systems, one-dimensional Logistic equation and two-dimensional Henon map were used. The optimizations were realized in several ways, each one for another set of parameters of evolution algorithms or separate cost functions. The evolutionary algorithm SOMA (self-organizing migrating algorithm) was used in four versions. For each version simulations were repeated several times to show and check for robustness of the applied method.

Evolutionary Game Theory.

Abstract: * The figures in Sections VII and IX were created using Dynamo [184] and VirtualLabs [92], respectively. I am grateful to Caltech for its hospitality as I completed this article, and I gratefully acknowledge financial support under NSF Grant SES-0617753.

A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

Abstract: The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach

Abstract: Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, ie, to directly solve the target data mining problem This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters The authors first offer introductory overviews on data mining, focusing on rule induction methods, and on evolutionary algorithms, focusing on genetic programming They then examine the conventional use of evolutionary algorithms to discover classification rules or related types of knowledge structures in the data, before moving to the more ambitious objective of their research, the design of a new genetic programming system for automating the design of full rule induction algorithms They analyze computational results from their automatically designed algorithms, which show that the machine-designed rule induction algorithms are competitive when compared with state-of-the-art human-designed algorithms Finally the authors examine future research directions This book will be useful for researchers and practitioners in the areas of data mining and evolutionary computation

A Bacterial Evolutionary Algorithm for automatic data clustering

Abstract: This paper describes an evolutionary clustering algorithm, which can partition a given dataset automatically into the optimal number of groups through one shot of optimization. The proposed method is based on an evolutionary computing technique known as the Bacterial Evolutionary Algorithm (BEA). The BEA draws inspiration from a biological phenomenon of microbial evolution. Unlike the conventional mutation, crossover and selection operaions in a GA (Genetic Algorithm), BEA incorporates two special operations for evolving its population, namely the bacterial mutation and the gene transfer operation. In the present context, these operations have been modified so as to handle the variable lengths of the chromosomes that encode different cluster groupings. Experiments were done with several synthetic as well as real life data sets including a remote sensing satellite image data. The results estabish the superiority of the proposed approach in terms of final accuracy.

Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster

Abstract: In this paper, Multi-objective evolutionary programming (MOEP) using fuzzy rank-sum with diversified selection is introduced. The performances of this algorithm as well as MOEP with non-domination sorting on the set of benchmark functions provided for CEC2009 Special Session and competition on Multi-objective Optimization are reported. With this rank-sum sorting and diversified selection, the speed of the algorithm has increased significantly, in particular by about twenty times on five objective problems when compared with the implementation using the non-domination sorting. Beside this, the proposed approach has performed either comparable or better than the MOEP with non-domination sorting.

A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding

Abstract: DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simultaneously several constraints, such as physical, chemical and logical constraints. In this paper, a novel quantum chaotic swarm evolutionary algorithm (QCSEA) is presented, and is first used to solve the DNA sequence optimization problem. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm cannot only avoid the disadvantage of easily getting to the local optional solution in the later evolution period, but also keeps the rapid convergence performance. The simulation results demonstrate that the proposed quantum chaotic swarm evolutionary algorithm is valid and outperforms the genetic algorithm and conventional evolutionary algorithm for DNA encoding.

Evolutionary programming with ensemble of explicit memories for dynamic optimization

Abstract: This paper presents the evolutionary programming with an ensemble of memories to deal with optimization problems in dynamic environments. The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving directions. Diversity of the population is enhanced by an ensemble of external archives that serve as short-term and long-term memories. The archive members also act as the basic solutions when environmental changes occur. The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results are presented.

On the efficiency of evolutionary fuzzy clustering

Abstract: This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

Implementation of parallel genetic algorithms on graphics processing units

Abstract: In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Units (GPUs) which are available and installed on ubiquitous personal computers. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in GPU and thus our parallel HGA can be executed effectively and efficiently. We suggest and develop the novel pseudo-deterministic selection method which is comparable to the traditional global selection approach with significant execution time performance advantages.We perform experiments to compare our parallel HGA with our previous parallel FEP (Fast Evolutionary programming) and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations of HGA are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness and efficiency of the pseudo-deterministic selection method is also studied.