Data Mining - Concepts and Techniques.

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Optimal power flow: a bibliographic survey I

Optimal power flow using differential evolution algorithm

Graphical abstractDisplay Omitted HighlightsProvide an updated and systematic review of distributed evolutionary algorithms.Classify the models into population and dimension-distributed groups semantically.Analyze the parallelism, search behaviors, communication costs, scalability, etc.Highlight recent research hotspots in this field.Discuss challenges and potential research directions in this field. The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

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Distributed evolutionary algorithms and their models

Differential evolution (DE) is a promising evolutionary algorithm for solving optimal reactive power flow problems, but it requires relatively large population to avoid premature convergence. In order to overcome this disadvantage, a novel decomposition and coordination method based on the cooperative co-evolutionary architecture and the voltage-var sensitivity-based power system decomposition technique is proposed and incorporated with DE in this paper. It is implemented with a three-level parallel computing topology on a PC-cluster. Based on the IEEE 118-bus system test case, the effectiveness of the proposed method has been verified by comparison with the parallel basic DE not using the decomposition and coordination technique

Parallel Optimal Reactive Power Flow Based on Cooperative Co-Evolutionary Differential Evolution and Power System Decomposition

Differential evolution (DE) is studied in detail for optimal reactive power flow (ORPF) problems. The concept, mechanism, and parameter setting of DE are discussed. Based on the IEEE 14-, 30- and 57-bus system test cases, DE is compared with some basic or improved evolutionary algorithms that have been applied to ORPF. It is found that DE is generally a good algorithm for ORPF and worthy of more attention. However, it is also found that DE requires relatively large populations to avoid premature convergence. The impact of this shortcoming is made clear in the IEEE 118-bus system test case. The effectiveness of parallel computing technology for speeding up the computation of DE-based ORPF is also analysed.

Study of differential evolution for optimal reactive power flow

Some recently developed evolutionary programming (EP) techniques for the optimal reactive power flow (ORPF) problem are investigated. By comparing the performances of four control schemes of the strategy parameter (CSSPs), two principles for designing effective CSSPs are identified and are further used to develop two improved CSSPs. The effectiveness of the Levy distribution based technique of adaptive fast EP is also studied. The investigations are conducted with simulations on the ORPF problems of the IEEE 14-, 30- and 118-bus standard test systems.

C.H. Liang

Papers

Parallel Optimal Reactive Power Flow Based on Cooperative Co-Evolutionary Differential Evolution and Power System Decomposition

Study of differential evolution for optimal reactive power flow

Comparison and improvement of evolutionary programming techniques for power system optimal reactive power flow