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Hybrid of comprehensive learning particle swarm optimization and SQP algorithm for large scale economic load dispatch optimization of power system

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Science China(Information Sciences) 2010年第8期53卷 1566-1573页

作者： WANG Yu , LI Bin & YUAN Bo Department of Electronic Science and Technology, School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China 1. Department of Electronic Science and Technology School of Information Science and Technology University of Science and Technology of China Hefei 230027 China

Economic load dispatch (ELD) is an important optimization task in power systems. In the previous works, various researchers attempted to address this problem by both mathmatical and heuristic optimization algorithms. However, there are still two practically important issues that have not attracted sufficient attention: 1) the stability of these algorithms cannot be effectively ensured; 2) the performance of these algorithms on large scale ELD optimization tasks remains to be unsatisfactory. CLPSO is an effective global optimization algorithm. To strengthen the convergence ability of CLPSO, the sequential quadratic programming (SQP) is introduced into it. This results in a new algorithm hybrid of comprehensive learning particle swarm optimization and sequential quadratic programming (SQP-CLPSO). To assess the performance of SQP-CLPSO, it is compared with several state-of-the-art evolutionary algorithms (EAs) on the classical ELD optimization problems. Experimental results show that SQP-CLPSO has very good abilities of convergence, diversity maintainence and scalability, which make it suitable for complex ELD problems.

关键词： particle swarm optimization comprehensive learning particle swarm optimization sequential quadratic programming local search power systems economic load dispatch

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Maximum Likelihood DOA Estimation in Wireless Sensor Networks Using comprehensive learning particle swarm optimization Algorithm 1

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Roula, Srinivash Gantayat, Harikrishna Panigrahi, T. Panda, G. Natl Inst Sci & Technol Dept ECE Berhampur 761008 Orissa India Indian Inst Technol Sch Elect Sci Bhubaneswar Odisha India

ISBN: (纸本)9788132222026;9788132222019

Direction of arrival (DOA) estimation is one of the challenging problem in wireless sensor networks. Several methods based on maximum likelihood (ML) criteria have been established in literature. Generally, to obtain the ML solutions, the DOAs must be estimated by optimizing a complicated nonlinear multimodal function over a high-dimensional problem space. comprehensive learning particle swarm optimization (CLPSO) based solution is proposed here to compute the ML functions and explore the potential of superior performances over traditional PSO algorithm. Simulation results confirms that the CLPSO-ML estimator is significantly giving better performance compared to conventional method like MUSIC in various scenarios at less computational costs.

关键词： Wireless sensor networks Maximum likelihood DOA estimation comprehensive learning particle swarm optimization

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Clustering in Mobile Ad Hoc Networks Using comprehensive learning particle swarm optimization (CLPSO)

Clustering in Mobile Ad Hoc Networks Using Comprehensive Lea...

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International Conference on Future Generation Communication and Networking/International Conference on Advanced Communication and Networking (FGCN/ACN 2009)

作者： Shahzad, Waseem Khan, Farrukh Aslam Siddiqui, Abdul Basit FAST Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan

ISBN: (纸本)9783642108433

In this work, we propose a comprehensive learning particle swarm optimization (CLPSO) based weighted clustering algorithm for mobile ad hoc networks. It finds the optimal number of clusters to efficiently manage the resources of the network. The proposed CLPSO based clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of the mobile nodes. A weight is assigned to each of these parameters of the network. Each particle contains information about the cluster-heads and the members of each cluster. The simulation results are compared with two other well-known clustering algorithms. Results show that the proposed technique works better than the other techniques especially in dense networks.

关键词： Clustering ad hoc networks comprehensive learning particle swarm optimization

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Multi-objective comprehensive learning particle swarm optimization based on Summation of Normalized Objectives and Diversified Selection

Multi-objective Comprehensive Learning Particle Swarm Optimi...

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26th Chinese Control and Decision Conference (CCDC)

作者： Zheng, Bo Qu, B. Y. Liang, J. J. Song, Hui Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9781479937066

In this paper, a fast-sorting method called summation of normalized objectives and diversified selection (SNOV-DS) is embedded in comprehensive learning particle swarm optimization (CLPSO) to solve multi-objective problems. Due to this method, the simulation time will be decreased. The convergence to true Pareto front and the spread of solutions can also be improved. The algorithm is tested on a set of commonly used multi-objective benchmark functions. The simulation results show that the proposed algorithm is competitive in terms of both performance and running speed.

关键词： Evolutionary Algorithms Multi-objective optimization comprehensive learning particle swarm optimization non-domination sorting

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Weighted Clustering using comprehensive learning particle swarm optimization for Mobile Ad Hoc Networks

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2010年第1期3卷 61-70页

作者： Shahzad, Waseem Khan, Farrukh Aslam Siddiqui, Abdul Basit FAST Natl Univ Comp & Emerging Sci Dept Comp Sci Islamabad Pakistan

A mobile Ad-hoc network consists of dynamic nodes that can move freely. These nodes communicate with each other without a base station. In this paper, we propose a comprehensive learning particle swarm optimization (CLPSO) based clustering algorithm for mobile ad hoc networks. It has the ability to find the optimal or near-optimal number of clusters to efficiently manage the resources of the network. The cluster-heads do the job of routing network packets within the cluster or to the nodes of other clusters. The proposed CLPSO based clustering algorithm takes into consideration the transmission power, ideal degree, mobility of the nodes and battery power consumption of the mobile nodes. It is a weighted clustering algorithm that assigns a weight to each of these parameters of the network. Each particle of the swarm contains information about the cluster-heads and the members of each cluster. It uses the evolutionary capability to optimize the number of clusters. We compare the simulation results with two other well-known clustering algorithms. The results show that the proposed technique is effective and works better than the other two approaches.

关键词： Clustering cluster-heads ad hoc networks comprehensive learning particle swarm optimization routing

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Quasi-oppositional symbiotic organism search algorithm applied to load frequency control

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swarm AND EVOLUTIONARY COMPUTATION 2017年 33卷 46-67页

作者： Guha, Dipayan Roy, Provas Banerjee, Subrata Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Jalpaiguri Govt Engn Coll Dept Elect Engn Jalpaiguri W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

The present work approaches a relatively new optimization scheme called "quasi-oppositional symbiotic organism search (QOSOS) algorithm", for the first time, to find an optimal and effective solution for load frequency control (LFC) problem of the power system. The symbiotic organism search (SOS) algorithm works on the effect of symbiotic interaction strategies adopted by an organism to survive and propagate in the ecosystem. To avoid the suboptimal solution and to accelerate the convergence speed, the theory of quasi-oppositional based learning (Q-OBL) is integrated with original SOS and used to solve the LFC problem. To demonstrate the effectiveness of QOSOS algorithm, two-area interconnected power system with nonlinearity effect of governor dead band and generation rate constraint is considered at the first instant, followed by the four-area power system showing the consequence of load perturbation. The structural simplicity, robust performance and acceptability of well-popular proportional-integral-derivative (PID) controller enforce to implement it as a secondary controller for the present analysis. The success of QOSOS algorithm is established by comparing the dynamic performances of concerned power system with those obtained by some recently published algorithms available in the literature. Furthermore, the robustness and sensitivity are analyzed for the concerned power system to judge the efficacy of the proposed QOSOS approach.

关键词： Load frequency control Symbiotic organism search Quasi-oppositional based learning comprehensive learning particle swarm optimization Krill herd algorithm Robustness analysis

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Kernel optimization for Reducing Core Vector Machine Classification Error

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NEURAL PROCESSING LETTERS 2023年第7期55卷 10011-10036页

作者： Afshin, Babak Shiri, Mohammad Ebrahim Layeghi, Kamran Haj Seyyed Javadi, Hamid Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran Shahed Univ Dept Math & Comp Sci Tehran Iran

Core vector machine (CVM) is considered as a popular machine learning technique, mostly used for classification and regression. Its generalized form can be either applied to kernel methods or used with linear and nonlinear kernels. As a classifier, both the kernel function and its parameters can significantly affect the accuracy of CVM classification. In this paper, a metaheuristic kernel core vector machine (MKCVM) was applied by using comprehensive learning particle swarm optimization. It was done through an innovative parameter selection technique in order to obtain a proper kernel function based on a nonlinear combination of standard kernel functions. The CVM true classification rate and geometric mean accuracy were adopted as the fitness criteria of the proposed approach, to be maximized. In addition, some benchmark datasets were used to evaluate the proposed approach. Based on the experimental results, the proposed method could decrease CVM classification error and increase its geometric mean accuracy. This has a comparative performance rather than support vector machine with lower memory and computational complexity. Moreover, MKCVM showed the acceptable capability to deal with imbalanced data. So, it had a good ability for handling binary and multiclass data classification.

关键词： Core vector machine Kernel Classification comprehensive learning particle swarm optimization Geometric mean accuracy Imbalanced data

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optimization of a nonuniformly slotted rectangular waveguide antenna

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2008年第7期50卷 1935-1937页

作者： Hwang, Keum Cheol Samsung Thales R&D Ctr HW Grp 1 Yongin 449880 Gyeonggi Do South Korea

optimization of a nonuniformly slotted rectangular waveguide antenna is performed using comprehensive learning particle swarm optimization (CLPSO) for the design of a high-efficiency, low-sidelobe antenna with a cosecant-squared beam. Mode-snatching method and the Fourier transform are used to analyze the nonuniformly slotted rectangular waveguide. The computational results show that the proposed optimizer with a fast convergent series solver is useful for the design and optimization of nonuniform slot arrays. (C) 2008 Wiley Periodicals, Inc.

关键词： rectangular waveguide nonuniform slots comprehensive learning particle swarm optimization

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Switched System Modeling for the Radar Antenna Servo Turntable 10

Switched System Modeling for the Radar Antenna Servo Turntab...

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10th IEEE Conference on Industrial Electronics and Applications

作者： Zhang, Qian Wang, Qunjing Li, Guoli Anhui Univ Sch Elect Engn & Automat Hefei 230039 Peoples R China Anhui Univ Engn Res Ctr Power Qual Minist Educ Hefei 230039 Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Techn Hefei 230039 Peoples R China

ISBN: (纸本)9781479983896

The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna servo turntable, such as low-speeding, changing-over, acceleration or deceleration etc. In this paper, a kind of switched model is introduced to describe the relationship between input and output signals in complex working conditions for the turntable. Then the parameters identification of switched system is summarized as a Constrained Multi-Objective Problem (CMOP). Further, the comprehensive learning particle swarm optimization (CLPSO) is applied to the proposed CMOP to obtain a set of appropriate parameters. Finally, the simulation and quality of fitness calculation results demonstrate the precision of the switched model and effectiveness of the identification algorithm.

关键词： turntable switched system parameter identification constrained multi-objective problem comprehensive learning particle swarm optimization

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Optimal Operation of PV-Diesel-Battery MG Based on Fuzzy comprehensive Evaluation 13

Optimal Operation of PV-Diesel-Battery MG Based on Fuzzy Com...

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13th International Conference on Ecological Vehicles and Renewable Energies (EVER)

作者： Zhang, Qian Ding, Jinjin Li, Guoli Anhui Univ Dept Elect Engn & Automat Hefei 230601 Peoples R China Anhui Elect Power Res Inst Hefei 230061 Peoples R China AHU Engn Res Ctr Power Qual Minist Educ Hefei 230601 Peoples R China

ISBN: (纸本)9781538659663

This paper focuses on the environmental and economic optimal operation of microgrid (MG) in various scenarios. An AC/DC hybrid MG including photovoltaic, diesel generator, lithium battery, electric vehicle charging points is analyzed. Considering the operation constrains of MG, a constrained multi-objective optimization problem (CMOP) is built. The optimization objectives of the proposed CMOP are the fuel cost, depreciation expense and emission cost of distributed generators. The fuzzy comprehensive evaluation is introduced for conversion from the multi-objective problem to a single-objective one. Then the comprehensive learning particle swarm optimization (CLPSO) is applied for the solutions of distributed generators' outputs. optimization results in grid-connected and islanded modes demonstrate the effectiveness of the proposed models, methods, and algorithm.

关键词： MG economic optimal operation fuzzy comprehensive evaluation comprehensive learning particle swarm optimization

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