Nature-inspired meta-heuristics for power system optimization ab 74.9 EURO Power System Optimization
The emergence of multiple web pages day by day leads to the development of the Semantic Web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the Resource Description Framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving meta-heuristic algorithms become an alternate to the traditional query optimization methods. This book focuses on the problem of query optimization of semantic web data. An efficient nature inspired algorithm called Adaptive Cuckoo Search (ACS) for querying and generating optimal query plan for large RDF graphs is discussed in this book.
Number of papers published in International Journals 1. M. Basu, "Fuel Constrained Economic Emission Load Dispatch using Hopfield Neural Networks", Journal of Electric Power System Research, Vol. 63, August 2002. 2. M. Basu, "Hopfield Neural Networks for Optimal Scheduling of Fixed Head Hydrothermal Power Systems", Journal of Electric Power Systems Research, Vol. 64, January 2003. 3. M. Basu, "An Interactive Fuzzy Satisfying-Based Simulated Annealing Technique for Economic Emission Load Dispatch with Nonsmooth Fuel Cost and Emission Level Functions", Journal of Electric Power Components and Systems, Vol. 32, No. 2 February 2004. 4. M. Basu, "Multiobjective Generation Scheduling of Fixed Head Hydrothermal Power Systems through an Interactive Fuzzy Satisfying method and Evolutionary Programming Technique", Journal of Electric Power Components and Systems, Vol. 32, No. 12 December 2004. 5. M. Basu, "Goal- Attainment Method Based on Simulated Annealing Technique for Economic-Environmental-Dispatch of Hydrothermal Power Systems with Cascaded Reservoirs", Journal of Electric Power Components and Systems, Vol. 32, No. 12 December 2004. 6. M. Basu, "An Interactive Fuzzy Satisfying
Particle swarm optimization (PSO) is a very popular, randomized, nature-inspired meta-heuristic for solving continuous black box optimization problems. The main idea is to mimic the behavior of natural swarms like, e. g., bird flocks and fish swarms that find pleasant regions by sharing information. The movement of a particle is influenced not only by its own experience, but also by the experiences of its swarm members.In this thesis, we study the convergence process in detail. In order to measure how far the swarm at a certain time is already converged, we define and analyze the potential of a particle swarm. This potential analysis leads to the proof that in a 1-dimensional situation, the swarm with probability 1 converges towards a local optimum for a comparatively wide range of objective functions. Additionally, we apply drift theory in order to prove that for unimodal objective functions the result of the PSO algorithm agrees with the actual optimum in k digits after time O(k). In the general D-dimensional case, it turns out that the swarm might not converge towards a local optimum. Instead, it gets stuck in a situation where some dimensions have a potential that is orders of magnitude smaller than others. Such dimensions with a too small potential lose their influence on the behavior of the algorithm, and therefore the respective entries are not optimized. In the end, the swarm stagnates, i. e., it converges towards a point in the search space that is not even a local optimum. In order to solve this issue, we propose a slightly modified PSO that again guarantees convergence towards a local optimum.
This book presents artificial immune system for optimal scheduling of thermal plants in coordination with fixed head hydro units. Numerical results for two test systems have been presented to demonstrate the performance of the proposed method. Results obtained from the proposed method have been compared to those obtained using differential evolution, particle swarm optimization and evolutionary programming technique.
This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book's third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.
This work concentrates on application of nature inspired meta-heuristic algorithms for graph based problems for optimization. The applications are concentrated on multi-objective formulation and coordinated swarm intelligence focusing on road networks. Multi-objective formulation is based on real life constraints and other road network related limitations. These approaches improved previous works and introduced novel algorithms in this field.
Traditional optimization methods are no longer adequate to solve complex real life problems, as most of them involve nonlinear, discontinuous, non- differentiable, nonconvex, multiobjective functions with mixed variables in their model formulation. Over the last few years, the use of nature inspired meta-heuristic algorithms for systems optimization has increased tremendously, and "swarm intelligence and evolutionary computing techniques" are rapidly emerging as powerful tools for solving practical problems. This book describes efficient computational techniques based on Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) principles for single and multiple criterion optimization, and hybrid soft-computing techniques such as PSO Neural Network (PSO-NN), Adaptive Network Fuzzy Inference System (ANFIS) for hydrologic forecasting and demonstrates their applications to case studies in reservoir systems operation. This book is intended for people who are willing to learn new technology to solve complex real life problems and especially useful to professional in water resources field.
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.