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international workshop on multilevel optimization - algorithms and applications

作者： Chang, YoungJung Sahinidis, Nikolaos V. Univ Illinois Dept Chem & Biomol Engn Urbana IL 61801 USA

In this paper, we develop a global optimization methodology to solve stabilization problems. We first formulate stabilization problems as bilevel programming problems. By invoking the Hurwitz stability conditions, we reformulate these bilevel programs to equivalent single-level nonconvex optimization programs. The branch-and-reduce global optimization algorithm is finally applied to these problems. Using the proposed methodology, we report improved solutions for two feedback stabilization problems from the literature. In addition, we improve the lower bound of the stabilizability parameter of the Belgian chocolate problem from the previous best known 0.96 to 0.973974.

关键词： stability simultaneous stabilization linear controller design global optimization Belgian chocolate problem

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Comparative analysis between modern heuristics and hybrid algorithms

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COMPEL-THE international JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2007年第2期26卷 259-268页

作者： Fulginei, Francesco Riganti Salvini, Alessandro Univ Roma Tre Dipartimento Elettron & Applicata Rome Italy

Purpose - The purpose of the present paper is to show a comparative analysis of classical and modem heuristics such as genetic algorithms, simulated annealing, particle swarm optimization and bacterial chemotaxis, when they are applied to electrical engineering problems. Design/methodology/approach - Hybrid algorithms (HAs) obtained by a synergy between the previous listed heuristics, with the eventual addiction of the Tabu Search, have also been compared with the single heuristic performances. Findings - Empirically, a different sensitivity for initial values has been observed by changing type of heuristics. The comparative analysis has then been performed for two kind of problems depending on the dimension of the solution space to be inspected. All the proposed comparative analyses are referred to two corresponding different cases: Preisach hysteresis model identification (high dimension solution space) and load-flow optimization in power systems (low dimension solution space). Originality/value - The originality of the paper is to verify the performances of classical, modem and hybrid heuristics for electrical engineering applications by varying the heuristic typology and by varying the typology of the optimization problem. An original procedure to design a HA is also presented.

关键词： genetics algorithmic languages simulation optimization techniques

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Particle Swarm optimization using adaptive mutation

Particle Swarm Optimization using adaptive mutation

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19th international Conference on Database and Expert Systems applications

作者： Pant, Millie Thangaraj, Radha Abraham, Ajith Indian Inst Technol Roorkee Dept Paper Technol Roorkee Uttar Pradesh India Norwegian Univ Sci & Technol Ctr Excellence Quantifiable Qual Serv Trondheim Norway

ISBN: (纸本)9780769532998

Two new variants of Particle Swarm optimization (PSO) called AAPSO1 and AAPSO2 are proposed for global optimization problems. Both the algorithms use adaptive mutation using Beta distribution. AMPSO1 mutates the personal best position of the swarm and AMPSO2, mutates the global best swarm position. The performance of proposed algorithms is evaluated on twelve unconstrained test problems and three real life constrained problems taken from the field of Electrical Engineering. The numerical results show the competence of the proposed algorithms with respect some other contemporary techniques.

关键词： Particle swarm optimization (PSO)

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DYNAMIC-PROGRAMMING AND GRAPH optimization PROBLEMS

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COMPUTERS & MATHEMATICS WITH applications 1994年第9-10期27卷 53-58页

作者： HU, TC MORGENTHALER, JD Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093-0114 U.S.A.

Several classes of graph optimization problems, which can be solved using dynamic programming, are known to have more efficient tailor-made algorithms. This paper discusses four such classes and the underlying constraints on their subproblem interrelationships that yield these efficient algorithms. These classes are also extended to handle more general cost functions.

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A General Framework for Intelligent optimization algorithms Based on multilevel Evolutions 13th

A General Framework for Intelligent Optimization Algorithms ...

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13th international Conference on Swarm Intelligence (ICSI)

作者： Wang, Chenchen Chen, Caifeng Lun, Ziru Ye, Zhanyu Liu, Qunfeng Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

Intelligent optimization is a kind of global optimization algorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimization algorithms have been proposed based on a large mount of animals' or plants' behaviors. This phenomenon shows the prosperity of this field, but bring issues about these algorithms' analysis and applications. We believe an extensive development stage has passed in the field of intelligent optimization, and more theoretical analysis and deep understanding about these algorithms become favorite. In this paper, we try to build a general framework for all population-based global optimization algorithms. This framework employs the idea of multilevel evolution, and therefore it can include not only the traditional bio-inspired evolution algorithms which often only evolute in a single level of search space, but also those population-based algorithms adopt data-driven strategies or cultural evolutions. By the help of the proposed framework, we can classify all population-based global optimization algorithms into three types, and improve the traditional algorithms. In this paper, this framework is then applied to the popular particle swarm optimization, and a modified particle swarm optimization with three-level of evolutions is proposed. Numerical results show that the modified algorithm improves the original one significantly.

关键词： Intelligent optimization algorithms Population-based optimization algorithms General framework multilevel evolutions

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Study on Comparison of Various optimization algorithms for Minimizing THD of the Hybrid Wind Solar Power Generation System 5

Study on Comparison of Various Optimization Algorithms for M...

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5th international Conference on Inventive Computation Technologies (ICICT)

作者： Ravikumar, S. Vennila, H. Deepak, R. Nurul Islam Ctr Higher Educ Dept EEE Kumaracoil Tamil Nadu India Coll Engn Dept EEE Adoor Kerala India

ISBN: (数字)9781728146850

ISBN: (纸本)9781728146850

Electricity plays an indispensable role in human lives. Due the increasing need for electricity in domestic, commercial and industrial applications and the deletion of conventional sources,the power generation system is switched on to systems with renewable energy sources. Therefore the power quality problems arises and research has been going on to improve the power quality. This paper is a study about the various power quality improving algorithms applied to the hybrid wind solar power generation with multilevel inverters. in comparison with various optimization algorithms, more control parameters with a new algorithm called Rider optimization Algorithm (ROA) is suggested.

关键词： Rider optimization algorithm hybrid power system multilevel inverters Total harmonics distortion (THD)

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Multiuser transmit optimization for multicarrier broadcast channels: Asymptotic FDMA capacity region and algorithms

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IEEE TRANSACTIONS ON COMMUNICATIONS 2004年第6期52卷 922-930页

作者： Hoo, LMC Halder, B Tellado, J Cioffi, JA Broadcom Corp Sunnyvale CA 94085 USA Telicos Corp Santa Clara CA 95050 USA Stanford Univ STAR Lab Stanford CA 94305 USA

We derive optimal and suboptimal multiuser transmit-optimization methods for a multicarrier broadcast channel with intersymbol interference under the frequency-division multiple-access (FDMA) restriction. The general FDMA-based multicarrier broadcast problem is formulated as a maximum weighted rate-sum problem. Given each user's subchannel assignment, the optimal transmit strategy is achieved by multilevel waterfilling. Unfortunately, the problem of finding the optimal subchannel assignments is combinatorial. However, by relaxing the FDMA restriction, we obtain a convex reformulation that allows for efficient computation of the optimal solution, and therefore, a characterization of the FDMA capacity region for a broadcast channel. If all users share the same transmission medium, we prove that the optimal frequency partitioning among the users has an ordered structure that can be exploited to significantly reduce the computational complexity. To make multiuser transmit-optimization schemes practical for applications with relatively fast time-varying user data-rate requirements or priorities, further reduction in computational complexity is necessary. This is achieved by restricting the energy distribution to be constant across the used subchannels. Simulations indicate the low-complexity constant-energy methods presented are very robust, and suffer from negligible performance loss.

关键词： broadcast channel constant-energy optimization frequency-division multiple access (FDMA) multilevel waterfilling multiuser loading multiuser transmit optimization orthogonal frequency-division multiplexing (OFDM)

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Hybrid evolutionary algorithm for solving general variational inequality problems

Hybrid evolutionary algorithm for solving general variationa...

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international workshop on multilevel optimization - algorithms and applications

作者： Majig, Mend-Amar Hedar, Abdel-Rahman Fukushima, Masao Kyoto Univ Grad Sch Informat Dept Appl Math & Phys Kyoto 606 Japan

This paper considers the problem of finding as many as possible, hopefully all, solutions of the general (i.e., not necessarily monotone) variational inequality problem (VIP). Based on global optimization reformulation of VIP, we propose a hybrid evolutionary algorithm that incorporates local search in promising regions. In order to prevent searching process from returning to the already detected global or local solutions, we employ the tunneling and hump-tunneling function techniques. The proposed algorithm is tested on a set of test problems in the MCPLIB library and numerical results indicate that it works well in practice.

关键词： variational inequality global optimization evolutionary algorithm local search tunneling function

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Query optimization to support data mining

Query optimization to support data mining

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8th international workshop on Database and Expert Systems applications

作者： Choenni, S Siebes, A Natl Aerospace Lab Amsterdam Netherlands

ISBN: (纸本)0818681489

In order to extract knowledge from databases, data mining algorithms heavily query the databases. Inefficient processing of these queries will inevitably have its impact on the performance of these algorithms, making them less valuable. In this paper, we describe an optimization framework for an efficient processing of queries generated by different data mining algorithms. We show how to take advantage of the physical organization of the database, the operators and the control structures used in an algorithm. Finally, we discuss how our framework fits into conventional query optimization frameworks.

关键词： Information retrieval

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Improvement of Two-swarm Cooperative Particle Swarm optimization Using Immune algorithms and Swarm Clustering 11

Improvement of Two-swarm Cooperative Particle Swarm Optimiza...

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IEEE 11th international workshop on Computational Intelligence and applications (IWCIA)

作者： Hayashida, Tomohiro Nishizaki, Ichiro Sekizaki, Shinya Takamori, Yuki Hiroshima Univ Grad Sch Engn Higashihiroshima Hiroshima Japan

ISBN: (纸本)9781728124292

Particle Swarm optimization (PSO) is useful as a method for solving optimization problems with continuous value variables because the convergence speed of solution search is fast. PSO is a evolutionary computation method in which individuals (particles) with position and velocity information are placed in the search space and acts for the purpose of finding an optimal solution with sharing information with other particles. This study constructs a particle swarm optimization method introducing the immune algorithms to improve the search capability of each particle and perform solution search more efficiently. To verify the usefulness of the proposed method, some numerical experiments are performed in this study.

关键词： Particle swarm optimization evolutionary computation immune algorithms two-swarm optimization

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