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Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow

ELECTRIC POWER COMPONENTS AND SYSTEMS 2012年第2期40卷 236-256页

作者： Roy, P. K. Mandal, D. Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 W Bengal India Birbhum Inst Engn & Technol Dept Elect Engn Suri W Bengal India

This article develops an efficient and reliable evolutionary programming algorithm, namely quasi-oppositional biogeography-based optimization, for solving optimal power flow problems. To improve the simulation results as well as the speed of convergence, opposition-based learning is incorporated in the original biogeography-based optimization algorithm. In order to investigate the performance, the proposed scheme is applied on optimal power flow problems of standard 26-bus, IEEE 118-bus, and IEEE 300-bus systems;and comparisons among mixed-integer particle swarm optimization, evolutionary programming, the genetic algorithm, original biogeography-based optimization, and quasi-oppositional biogeography-based optimization are presented. The results show that the new quasi-oppositional biogeography-based optimization algorithm outperforms the other techniques in terms of convergence speed and global search ability.

关键词： biogeography-based optimization optimal power flow opposition-based learning evolutionary programming mutation migration

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Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 830-838页

作者： Roy, P. K. Ghoshal, S. P. Thakur, S. S. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

In this paper, a Biogeography Based Optimization (BBO) technique is introduced to solve multi-constrained optimal reactive power flow (ORPF) problem in power system. ORPF is a multi-objective nonlinear optimization problem that minimizes the bus voltage deviation and real power loss. The feasibility of the proposed algorithm is demonstrated for IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over other well established population based optimization techniques like conventional particle swarm optimization (PSO), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA) and interior point based OPF (IP-OPF). (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Biogeography Based Optimization Optimal reactive power flow evolutionary programming Particle swarm optimization Migration Mutation

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Optimization of Tree-like Core Overlay in Hybrid-structured Application-layer Multicast

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2012年第12期6卷 3117-3132页

作者： Weng, Jianguang Zou, Xuelan Wang, Minhong Zhejiang Inst Media & Commun Hangzhou 310018 Peoples R China Univ Hong Kong Fac Educ Div Informat & Technol Studies Hong Kong Hong Kong Peoples R China

The tree topology in multicast systems has high transmission efficiency, low latency, but poor resilience to node failures. In our work, some nodes are selected as backbone nodes to construct a tree-like core overlay. Backbone nodes are reliable enough and have strong upload capacity as well, which is helpful to overcome the shortcomings of tree topology. The core overlay is organized into a spanning tree while the whole overlay is of mesh-like topology. This paper focuses on improving the performance of the application-layer multicast overlay by optimizing the core overlay which is periodically adjusted with the proposed optimization algorithm. Our approach is to construct the overlay tree based on the out-degree weighted reliability where the reliability of a node is weighted by its upload bandwidth (out-degree). There is no illegal solution during the evolution which ensures the evolution efficiency. Simulation results show that the proposed approach greatly enhances the reliability of the tree-like core overlay systems and achieves shorter delay simultaneously. Its reliability performance is better than the reliability-first algorithm and its delay is very close to that of the degree-first algorithm. The complexity of the proposed algorithm is acceptable for application. Therefore the proposed approach is efficient for the topology optimization of a real multicast overlay.

关键词： Tree-like core overlay application-layer multicast out-degree weighted reliability evolutionary programming

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Considering transmission loss for an economic dispatch problem without valve-point loading using an EP-EPSO algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2012年第Sup2期20卷 1259-1267页

作者： Muthu Vijaya Pandian, Sevugarathinam Thanushkodi, Keppanagowder Sri Krishna Coll Technol Coimbatore Tamil Nadu India Akshaya Coll Engn & Technol Coimbatore Tamil Nadu India

Economic dispatch (ED) is one of the most important optimization problems in a power system. The objective of ED is sharing the power demand among the online generators while keeping the minimum cost of generation as a constraint. The aim of this paper is to operate an electric power system as economically as possible within its security limits. This paper proposes the following 2 new particle swarm optimization (PSO) algorithms to solve a nonconvex economic dispatch problem: an efficient PSO is termed as efficient particle swarm optimization (EPSO), and a hybrid of evolutionary programming (EP) and EPSO is termed as EP-EPSO. Since ED was introduced, several methods have been used to solve these problems. However, none of these methods can provide an optimal solution because they become trapped at some local optima. Stochastic optimization techniques such as EPSO and EP have the advantage of a good convergent property. A significant speed-up can be obtained by the hybrid of these algorithms. The proposed techniques are tested on standard test systems available in the literature. The performance of the proposed EP-EPSO is compared with a) biogeography-based optimization, b) adaptive particle swarm optimization, c) the genetic algorithm, d) a 2-phase neural network, e) PSO with time-varying acceleration coefficients, f) NEW-PSO, and g) differential evolution with biogeography-based optimization. It is observed that the EP-EPSO has a higher convergence rate, advanced quality, and better optimal cost when compared to the other techniques. The considered ED problems have been solved, including transmission losses without valve-point loading effects.

关键词： Economic load dispatch evolutionary programming efficient particle swarm optimization power balance constraints

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Gravitational Search Algorithm Based Optimal Reactive Power Dispatch for Voltage Stability Enhancement

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2012年第9期40卷 956-976页

作者： Roy, P. K. Mandal, B. Bhattacharya, K. Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

In this article, an efficient and reliable optimization procedure based on the behaviors of swarm in nature, namely the gravitational search algorithm, is proposed for solving multi-objective optimal reactive power dispatch problems, which minimizes transmission loss while maintaining the quality of voltages. The gravitational search algorithm is based on Newton's law of gravity and interaction of masses. In the proposed algorithm, the searcher agents which are a collection of masses interact with each other using Newton's laws of gravity and motion. In order to investigate the performance of the proposed scheme, multi-objective optimal reactive power dispatch problems are solved. This new gravitational search algorithm method is tested on IEEE 57-bus and IEEE 118-bus power systems. Results obtained by the gravitational search algorithm are compared with two versions of genetic algorithms, three versions of differential evolution algorithms, four versions of particle swarm optimization algorithms and the seeker optimization algorithm It is observed from the test results that the proposed gravitational search algorithm approach converges to better solutions much faster than the earlier reported approaches.

关键词： biogeography-based optimization evolutionary programming gravitational search algorithm law of gravity law of motion optimal reactive power dispatch voltage stability margin

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evolutionary product unit neural networks for short-term wind speed forecasting in wind farms

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NEURAL COMPUTING & APPLICATIONS 2012年第5期21卷 993-1005页

作者： Hervas-Martinez, C. Salcedo-Sanz, S. Gutierrez, P. A. Ortiz-Garcia, E. G. Prieto, L. Univ Alcala de Henares Dept Signal Theory & Commun Alcala De Henares Spain Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain Iberdrola Renovables Energy Resources Dept Madrid Spain

Combinations of physical and statistical wind speed forecasting models are frequently used in wind speed prediction problems arising in wind farms management. Artificial neural networks can be used in these models as a final step to obtain accurate wind speed predictions. The aim of this work is to determine the potential of evolutionary product unit neural networks (EPUNNs) for improving the accuracy and interpretation of these systems. Traditional neural network and EPUNN approaches have been used to develop different wind speed prediction models. The results obtained using different EPUNN models show that the functional model and the hybrid algorithms proposed provide very accurate prediction compared with standard neural networks used to solve this regression problem. One of the main advantages of the application of these EPUNNs has been the possibility of obtaining some interpretation of the non-linear relation predicted by the model, as will be shown in real data of a wind farm in Spain.

关键词： Short-term wind speed forecasting Product unit neural networks evolutionary programming

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Solution of multi-objective optimal power flow using gravitational search algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第8期6卷 751-763页

作者： Bhattacharya, A. Roy, P. K. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This article presents application of an efficient and reliable heuristic technique inspired by swarm behaviours in nature namely, gravitational search algorithm (GSA) for solution of multi-objective optimal power flow (OPF) problems. GSA is based on the Newton's law of gravity and mass interactions. In the proposed algorithm, the searcher agents are a collection of masses that interact with each other using laws of gravity and motion of Newton. In order to investigate the performance of the proposed scheme, multi-objective OPF problems are solved. A standard 26-bus and IEEE 118-bus systems with three different individual objectives, namely fuel cost minimisation, active power loss minimisation and voltage deviation minimisation, are considered. In multi-objective problem formulation fuel cost and loss;fuel cost and voltage deviation;fuel cost, loss and voltage deviation are minimised simultaneously. Results obtained by GSA are compared with mixed integer particle swarm optimisation, evolutionary programming, genetic algorithm and biogeography-based optimisation. The results show that the new GSA algorithm outperforms the other techniques in terms of convergence speed and global search ability.

关键词： GSA algorithm search problems evolutionary programming Optimisation techniques reliable heuristic technique multiobjective problem formulation fuel cost voltage deviation minimisation swarm behaviours multiobjective OPF problems genetic algorithm multiobjective optimal power flow solution active power loss minimisation IEEE bus systems fuel cost minimisation Newton law of gravity and mass interactions biogeography-based optimisation gravitational search algorithm load flow mixed integer particle swarm optimisation Combinatorial mathematics Power systems

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Using genetical and cultural search to design unorganised machines

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evolutionary INTELLIGENCE 2012年第1期5卷 23-33页

作者： Bull, Larry Univ West England Dept Comp Sci Bristol BS16 1QY Avon England

In 1948 Turing presented a general representation scheme by which to achieve artificial intelligence-his unorganised machines. Significantly, these were a form of discrete dynamical system and yet dynamical representations remain almost unexplored within evolutionary computation. Further, at the same time as also suggesting that natural evolution may provide inspiration for search mechanisms to design machines, he noted that mechanisms inspired by the social aspects of learning may prove useful. This paper presents results from an investigation into using Turing's dynamical representation designed by evolutionary programming and a new imitation-based, i. e., cultural, approach. Moreover, the original synchronous and an asynchronous form of unorganised machines are considered.

关键词： A-type unorganised machines Discrete dynamical systems evolutionary programming Imitation

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Applying Evolution programming Search Based Software Engineering (SBSE) in Selecting the Best Open Source Software Maintainability Metrics

Applying Evolution programming Search Based Software Enginee...

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IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Bakar, A. D. Sultan, A. B. Zulzalil, H. Din, J. Univ Putra Malaysia Fac Comp Sci & Informat Technol Serdang 43400 Selangor Malaysia

ISBN: (纸本)9781467330336;9781467323024

The nature of an Open Source Software development paradigm forces individual practitioners and organization to adopt software through trial and error approach. This leads to the problems of coming across software and then abandoning it after realizing its lack of important qualities to suit their requirements or facing negative challenges in maintaining the software. These contributed by lack of recognizing guidelines to lead the practitioners in selecting out of the dozens available metrics, the best metric(s) to measure quality OSS. In this study, the novel results provide the guidelines that lead to the development of metrics model that can select the best metric(s) to predict maintainability of Open Source Software.

关键词： Open Source Software Search Base Software Engineering evolutionary programming Maintainability

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Use of an evolutionary Approach for Question Paper Template Generation

Use of an Evolutionary Approach for Question Paper Template ...

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IEEE 4th International Conference on Technology for Education (T4E)

作者： Paul, Dimple V. Naik, Shankar B. Rane, Priyanka Pawar, Jyoti D. DMs Coll Dept Comp Sci Mapusa Goa India Dhempe Coll Dept Comp Sci Miramar India Chowgule Coll M Sc IT Student Margoa India Goa Univ Dept Comp Sci & Tech Taleiegao India

ISBN: (纸本)9780769547596;9781467321730

This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation. By using the evolutionary computational search technique of evolutionary programming and educational taxonomies, this paper analyses and experimentally proves that the generated question paper templates are best suited for dynamic examination paper generation. This new technique outperforms traditional algorithms in terms of coverage of topics, learning domains and marks distribution in the generated question paper.

关键词： blooms taxonomy evolutionary programming question bank question paper template

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