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A Comparative Study of Different Motif Occurrence Models Applied to a Hybrid Multiobjective shuffle frog leaping algorithm

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COMPUTER JOURNAL 2016年第3期59卷 384-402页

作者： Gonzalez-Alvarez, David L. Vega-Rodriguez, Miguel A. Rubio-Largo, Alvaro Univ Extremadura Dept Comp & Commun Technol Caceres Spain Univ Nova Lisboa NOVA Informat Management Sch P-1200 Lisbon Portugal

The motif discovery problem (MDP) is an important biological optimization problem that has been addressed in numerous ways. However, it is important to note that when we address real complex optimization problems, we should adequately formulate them in order to provide real applicability to the developed techniques. In the particular case of MDP, as we do not know the size of the motifs and the number of repetitions that can be found in the sequences, we must not make any length or pattern-repetition assumptions. In addition, if we consider that it is practically impossible to adequately formulate an optimization problem with a single-objective function formulation, multiobjective optimization can be a good methodology to be considered. In this paper, we propose a novel hybrid multiobjective algorithm for tackling the MDP. Our main objective is to study the results achieved by our algorithm, analysing its performance when different motif occurrence models are considered. As we will see, experimental results on different sets of real instances will point out the advantages and disadvantages of each model, also checking how a more realistic definition of the optimized problem provides better quality biological results.

关键词： computational science swarm intelligence multiobjective optimization shuffle frog leaping algorithm motif discovery problem

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A modified shuffle frog leaping algorithm for multi-objective optimal power flow

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ENERGY 2011年第11期36卷 6420-6432页

作者： Niknam, Taher Narimani, Mohammad Rasoul Jabbari, Masoud Malekpour, Ahmad Reza Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Zarghan Branch Zarghan Iran

This paper presents an efficient and reliable evolutionary-based approach to solve the Optimal Power Flow (OPF) problem by considering the emission issue. The OPF problem has been widely used in power system operation and planning for determining electricity prices. Therefore, the conventional optimal power flow cannot meet the environmental protection requirements, because it only considers generation cost minimization. The multi-objective optimal power flow considers economical and emission issues. By adding the emission objective in the optimal power flow problem, this problem become more complicated than before and it needs to be solved with an accurate algorithm. This paper proposes an algorithm based on the shuffle frog leaping algorithm (SLFA) to solve the multi-objective OPF problem. Furthermore, this paper presents a modified SLFA called MSLFA algorithm which profits from a mutation in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The IEEE 30-bus test system is presented to illustrate the application of the proposed problem. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： shuffle frog leaping algorithm Optimal power flow Generation cost Emission

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An electricity price optimization model considering time-of-use and active distribution network efficiency improvements

AIMS ENERGY

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AIMS ENERGY 2025年第1期13卷 13-34页

作者： Li, Yan Su, Yaheng Zhao, Qixin Wuda, Bala Qu, Kaibo Tang, Lei Inner Mongolia Power Grp Co Ltd Hohhot 010010 Peoples R China Inner Mongolia Elect Power Econ & Technol Res Inst Hohhot 010010 Peoples R China Cent Southern China Elect Power Design Inst Co Ltd China Power Engn Consulting Grp Wuhan 430071 Peoples R China

To address the issues of high energy costs and inadequate system response speed in complex electricity markets, we propose an electricity price optimization model. This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the shuffle frog leaping algorithm. The work was based on multi-regional peak and valley data, and we selected Lanzhou, Guiyang, Beijing, Guangzhou, Shanghai, and Nanjing as typical representatives for systematic validation and analysis. Our findings were as follows: (1) The model demonstrated excellent convergence and stability during the electricity price optimization process, particularly under flat-rate price conditions. This model effectively avoided local optima traps and enhanced global search capability, achieving the dual goals of rapid convergence and high stability, and showed exceptional optimization efficiency and adaptability;(2) building upon its optimization performance, the model further improved the uniformity and diversity of the solution distribution, ensuring robustness and flexibility in global search ability. Moreover, by dynamically adjusting the price function and multi-level evaluation system, the model significantly optimized price elasticity, time-of-use pricing regulation efficiency, energy consumption paths, and the operational stability of the distribution network. The model exhibited high resilience and fine-grained control capabilities in the complex electricity market;(3) finally, based on the optimized electricity price strategy derived from training, the model reduced electricity costs and price volatility. Moreover, its superior performance in economic benefits and market adaptability was comprehensively validated through high-precision power consumption forecasting. We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.

关键词： electricity price optimization improved particle swarm optimization algorithm quantum- behaved particle swarm optimization algorithm shuffle frog leaping algorithm time-of-use pricing

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A new hybrid algorithm for optimal power flow considering prohibited zones and valve point effect

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ENERGY CONVERSION AND MANAGEMENT 2012年 58卷 197-206页

作者： Niknam, Taher Narimani, Mohammad Rasoul Azizipanah-Abarghooee, Rasoul Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In this paper, an effective and reliable algorithm, based on shuffle frog leaping algorithm (SFLA) and Simulated Annealing (SA) is proposed for solving the optimal power flow (OPF) problem with non-smooth and non-convex generator fuel cost characteristics. Also, the proposed OPF formulation contains detailed generator constraints including active and reactive power generation limits, valve loading effects, and Prohibited Operating Zones (POZs) of units. OPF is spontaneously a complicate optimization problem, and becomes more and more complex considering the above constraints. Therefore, it needs to be solved with an accurate algorithm. Recently researchers have presented a new evolutionary method called SFLA algorithm. The original SFLA often converges to local optima. In order to avoid this shortcoming we propose a new method that profits from SA algorithm to improve local search near the global optima. The possibility of convergence to global optima is increased using the proposed method. For validating the proposed algorithm, it has been examined on the standard IEEE 30-bus test systems. The hybrid SFLA-SA provides better results compared to the original SFLA. SA, and other methods recently reported in the literature as demonstrated by simulation results. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow Hybrid SFLA-SA Evolutionary algorithm shuffle frog leaping algorithm

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A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type

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ENERGY 2013年第1期49卷 119-136页

作者： Narimani, Mohammad Rasoul Azizipanah-Abarghooee, Rasoul Zoghdar-Moghadam-Shahrekohne, Behrouz Gholami, Kayvan Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Kermanshah Branch Kermanshah Iran Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran

This paper presents a new hybrid algorithm based on the Particle Swarm Optimization (PSO) and the shuffle frog leaping algorithms (SFLA) for solving the Optimal Power Flow (OFF) in power systems. In consequence of economical issues and increasing of the social welfare, the OPF problem is turning into a pretty remarkable problem and getting more and more important in power systems. The proposed optimization problem has considered the real conditions of power generation involving the prohibit zones, valve point effect and multi-fuel type of generation units. Increasing concerns over the environmental issues forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the OPF problem has become a multi-objective optimization problem. This paper takes advantages of the Pareto optimal solution and fuzzy decision making method in order to achieve the set of optimal solutions and best compromise solution, respectively. The presented algorithm is applied to 30, 57 and 118-bus test systems and the obtained results are compared with those in literature. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow Multi-objective Pareto optimal solution Hybrid optimization algorithm Particle swarm optimization shuffle frog leaping algorithm

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Discussion of "Application of excel solver for parameter estimation of the nonlinear Muskingum models" by Reza Barati

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第1期19卷 340-342页

作者： Hamedi, Farzan Bozorg-Haddad, Omid Orouji, Hosein Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran Iran

An accurate solution method is essential to the calibration of the nonlinear Muskingum model. Most of the earlier researchers have used inaccurate Euler’s solution method which is manipulated to get a better fit for observed Wilson’s data (1974). Euler’s method which is adopted by most previous researchers is not very accurate and results in unsuitable simulation based on the nonlinear Muskingum model as shown in this discussion. This study proposes fourth-order Runge-Kutta method as a suitable and accurate solution method for simulation stage. When more accuracy is needed, the structure of the Muskingum model can be modified to produce more degree of freedom in model calibration procedure. For this purpose, a new five-parameter nonlinear Muskingum model is proposed. The proposed model is easy to formulate and use. The results show that the improvement in the fit of the proposed nonlinear Muskingum model is substantial.

关键词： KSCE Journal Present Discussion Agricultural Engineer Harmony Search algorithm shuffle frog leaping algorithm

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Evaluating Distributed Generations in Utility Operation and Planning Issues Using a Novel Fusion PSO-SFLA algorithm 25

Evaluating Distributed Generations in Utility Operation and ...

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Australasian Universities Power Engineering Conference (AUPEC)

作者： Mahboubi-Moghaddam, Esmaeil Aghaei, Jamshid Muttaqi, Kashem M. Zoghdar-Moghadam-Shahrekohne, Behrouz Narimani, Mohammad Rasoul Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Wollongong APQRC SECTE Wollongong NSW 2522 Australia Missouri Univ Sci & Technol Elect Engn Rolla MO USA

ISBN: (纸本)9781479987252

Increasing demand of electrical energy has leaded to utilization of more and more Distributed generation (DG) sources in distribution systems. Since the locations and capacities of the DG sources connected to the distribution system profoundly impact on reducing system loss and improving system reliability, so placement and sizing indication of DGs is the most substantial process in distribution systems. By adding the reliability objective to this problem, it becomes more complicated than before and it needs to be solved with an accurate algorithm. To this reason, to solve the proposed problem a new approach based on the mixture of two algorithms named as Particle Swarm Optimization (PSO) and the shuffle frog leaping algorithm (SFLA) is applied in this paper. A meticulous performance analysis is fulfilled on a 33-bus system in order to demonstrate the effectiveness of the presented methodology.

关键词： component Distributed Generation Reliability Particle Swarm Optimization shuffle frog leaping algorithm

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