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Dynamic economic emission dispatch using nondominated sorting genetic algorithm-ii

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2008年第2期30卷 140-149页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents nondominated sorting genetic algorithm-ii for dynamic economic emission dispatch problem. This problem is formulated as a nonlinear constrained multi-objective optimization problem. nondominated sorting genetic algorithm-ii is proposed to handle dynamic economic emission dispatch problem as a true multi-objective optimization problem with competing and noncommensurable objectives. The proposed approach has a good performance in finding a diverse set of solutions and in converging near the true pareto-optimal set. Numerical results for a sample test system have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic economic emission dispatch problem in one single run. The comparison with the classical technique demonstrates the superiority of the proposed algorithm. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： dynamic economic dispatch dynamic economic emission dispatch multi-objective optimization nondominated sorting genetic algorithm-ii real-coded genetic algorithm

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Economic environmental dispatch of fixed head hydrothermal power systems using nondominated sorting genetic algorithm-ii

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APPLIED SOFT COMPUTING 2011年第3期11卷 3046-3055页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 W Bengal India

This paper presents nondominated sorting genetic algorithm-ii for economic environmental dispatch of fixed head hydrothermal power systems. The problem is formulated as a nonlinear constrained multi-objective optimization problem. nondominated sorting genetic algorithm-ii is proposed to handle economic environmental dispatch of fixed head hydrothermal power systems as a true multiobjective optimization problem with competing and noncommensurable objectives. Numerical results of two test systems demonstrate the capabilities of the proposed approach. Results obtained from the proposed approach have been compared with those obtained from strength pareto evolutionary algorithm 2, multiobjective differential evolution and previously obtained results. (C) 2010 Elsevier B. V. All rights reserved.

关键词： Hydrothermal system Multi-objective optimization nondominated sorting genetic algorithm-ii Real coded genetic algorithm

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Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-ii

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ENERGY 2014年 78卷 649-664页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents nondominated sorting genetic algorithm-ii for solving fuel constrained economic emission dispatch problem of thermal generating units. This is a multi-objective optimization problem which includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints are ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that one of the two objectives (i.e. fuel cost and emission level) may be increased while other may be decreased to serve the same power demand but this may well compensate for the penalty that might be otherwise imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results are compared with those obtained from strength pareto evolutionary algorithm 2. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Fuel constrained economic emission dispatch nondominated sorting genetic algorithm-ii Economic emission dispatch

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Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-ii

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 135-141页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents nondominated sorting genetic algorithm-ii for solving combined heat and power economic emission dispatch problem. The problem is formulated as a nonlinear constrained multi-objective optimization problem. nondominated sorting genetic algorithm-ii is proposed to handle economic emission dispatch as a true multi-objective optimization problem with competing and noncommensurable objectives. The proposed algorithm is illustrated for two test systems and the test results are compared with those obtained from strength pareto evolutionary algorithm 2. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Cogeneration Combined heat and power economic emission dispatch nondominated sorting genetic algorithm-ii

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D-NSGA-ii: Dual-Stage nondominated sorting genetic algorithm-ii 3rd

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3rd International Conference on Robot Intelligence Technology and Applications

作者： Lee, Ki-Baek Kwang Woon Univ Dept Elect Engn 20 Kwangwoon Ro Seoul South Korea

ISBN: (纸本)9783319168418;9783319168401

This paper proposes a novel multi-objective optimization algorithm, dual-stage nondominated sorting genetic algorithm-ii (D-NSGA-ii) for many-objective problems. Since the percentage of the nondominated solutions increases exponentially with the increasing number of objectives, just finding the nondominated solutions is not enough for solving many-objective problems. In other words, it is necessary to discriminate more meaningful ones from the other non-dominated solutions by additionally incorporating user preference into the algorithms. The proposed D-NSGA-ii can obtain not only user preference oriented, but also diverse nondominated solutions by introducing an additional stage of multi-objective optimization. The second stage employs the corresponding secondary objectives, global evaluation and crowding distance which were proposed in the previous research for representing the user's preference to a solution and the crowdedness around a solution, respectively. To demonstrate the effectiveness of the proposed algorithm, some benchmark functions are tested and the outcomes of the proposed D-NSGA-ii and the NSGA-ii are empirically compared. Experimental results show that D-NSGA-ii properly reflects the user's preference in the optimization process as well as the performance in terms of the diversity and solution quality is competitive with the NSGA-ii.

关键词： Multi-Objective Evolutionary algorithm Dual-Stage nondominated sorting genetic algorithm-ii User Preference Crowding Distance

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An Improved nondominated sorting genetic algorithm-ii for Multi-objective Flexible Job-shop Scheduling Problem 8

An Improved Nondominated Sorting Genetic Algorithm-II for Mu...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Luo, Shu Liu, Chongdang Zhang, Linxuan Fan, Yushun Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781538692769

This study focuses on the multi-objective flexible job-shop scheduling problem (MOFJSP) considering three objectives, namely maximal completion time, total workload of machines and maximal machine workload. An improved nondominated sorting genetic algorithm-ii (INSGA-ii) is developed to solve the MOFJSP. Specifically, a probability-based uniform crossover operator called "PEPPX" is designed to reach a tradeoff between exploration and exploitation. A novel "pruningregenerating" mechanism (PRM), which can remove the redundant solutions in an elitist population and generate a new population via variable neighborhood search (VNS), is proposed to maintain the population diversity. Three objective-oriented local search operators for the VNS are elaborately designed based on critical path. Furthermore, a left-shift decoding method is applied to attain active schedules. Numerical experiments including two case studies confirm the effectiveness of the proposed INSGA-ii.

关键词： multi-objective flexible job-shop scheduling nondominated sorting genetic algorithm-ii variable neighborhood search critical path

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Improved nondominated sorting genetic algorithm-ii for bi-objective flexible job-shop scheduling problem

Improved nondominated sorting genetic algorithm-II for bi-ob...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Luo, Shu Zhang, Linxuan Fan, Yushun Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781728125473

In modern manufacturing industry, not only production efficiency. but also fairness in wages should be emphasized so as to motivate the staff. To this end, this paper addresses the multi-objective flexible job shop scheduling problem (MOFJSP) aiming at minimizing the makespan and maximum wage gap among workers simultaneously. An improved nondominated sorting genetic algorithm-ii (INSGAii) is developed. The novelties are mainly presented as follows. First, a probability-based extended precedence preservative crossover operator (PEPPX) is designed for accelerating the convergence rate. Second, in order to solve the multi-modal problem, a "pruning-regenerating" mechanism (PRM) is developed to remove the redundant solutions in objective space and reinsert new solutions. Specifically, a novel metric called "encoding distance" is proposed to measure the diversity of solutions with exactly the same objective function values in chromosome encoding. Meanwhile, a critical path based variable neighborhood search (VNS) is designed to regenerate new solutions replacing the removed ones. Numerical experiments on a wide set of well-known benchmarks have confirmed the effectiveness and efficiency of the proposed INSGA-ii.

关键词： Multi-objective Multi-modal Flexible job-shop scheduling nondominated sorting genetic algorithm-ii Variable neighborhood search

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Non-dominated sorting genetic algorithm-ii for robust multi-objective optimal reactive power dispatch

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IET GENERATION TRANSMISSION & DISTRIBUTION 2010年第9期4卷 1000-1008页

作者： Zhihuan, L. Yinhong, L. Xianzhong, D. Huazhong Univ Sci & Technol Hubei Elect Power Secur & High Efficiency Key Lab Wuhan 430074 Hubei Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30318 USA

The concept of robust optimal solution is incorporated into multi-objective optimal reactive power dispatch (MORPD) for the consideration of uncertain load perturbations during system operations. Robust MORPD searches for solutions that are immune to parameter drifts and load changes. It uses information of load-increase directions to promote the stability of optimal solutions in the presence of load perturbations. Non-dominated sorting genetic algorithm-ii (NSGA-ii) is adopted to search for the robust Pareto solutions on a standard IEEE 118-bus system. The simulation validated the effectiveness of NSGA-ii for robust MORPD. NSGA-ii obtained Pareto solutions over the trade-off surface. The experimental results also indicated that the robust Pareto solutions are comparatively less sensitive to load perturbations in their neighbourhoods and can maintain their objective values against uncertain load perturbations. Robust MORPD can provide optimal solutions with a higher degree of stability in the face of perturbations and can be more practical in reactive power optimisation of the real-time operation systems.

关键词： robust Pareto solutions power generation dispatch robust multiobjective optimal reactive power dispatch power system security IEEE 118-bus system uncertain load perturbations NSGA-ii Power system control reactive power genetic algorithms nondominated sorting genetic algorithm-ii sorting Combinatorial mathematics Power system management, operation and economics reactive power optimisation Optimisation techniques Pareto optimisation

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Optimal Multivariable Two-Degree-of-Freedom Control of Electric Wheelchair Using Non-dominated sorting genetic algorithm-ii

Optimal Multivariable Two-Degree-of-Freedom Control of Elect...

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IEEE International Conference on Control Applications (CCA)

作者： Saadatzi, Mohammad Nasser Poshtan, Javad Saadatzi, Mohammad Hossein Iran Univ Sci & Technol Tehran Iran KN Toosi Univ Technol Tehran Iran

ISBN: (纸本)9781457710636

Electric wheelchairs (EW) experience various terrains surfaces and slopes as well as occupants with diverse weights. This, in turn, imparts a substantial amount of perturbation to the EW dynamics. In this paper we make use of a two-degree-of-freedom control architecture called disturbance observer (DOB) which reduces sensitivity to model uncertainties while enhancing rejection of disturbances which occur when entering slopes. The feedback loop which is designed via characteristic loci method (CLM) is then augmented with a DOB containing a parameterized low-pass filter. According to the disturbance rejection, sensitivity reduction, and noise rejection of the whole controller, three performance indices are defined which enable us to pick the filter's optimal parameters using a multi-objective optimization (MOO) approach called non-dominated sorting genetic algorithm-ii (NSGA-ii). Finally, experimental results show desirable improvement in stiffness and disturbance rejection of the proposed controller as well as its robust stability.

关键词： Approximation methods Brushless DC motors Control systems Low pass filters Noise Uncertainty characteristic loci method disturbance observer disturbance rejection elderly patients electric vehicles electric wheelchair feedback feedback loop genetic algorithms handicapped aids low-pass filters medical control systems mobile robots mobility-impaired patients multiobjective optimization multivariable control systems noise rejection nondominated sorting genetic algorithm-ii observers optimal control optimal multivariable two-degree-of-freedom control parameterized low-pass filter robust control robust stability sensitivity reduction wheelchairs

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Electric-bus charging stations multi-objective optimization planning on coupled power and traffic networks

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IET INTELLIGENT TRANSPORT SYSTEMS 2024年第4期18卷 619-629页

作者： Zhang, Kai Chen, Yaohui Cui, Chenqu Wu, Peng Miao, Lixin Chen, Bokui Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Guangdong Peoples R China Peng Cheng Lab Dept Network Intelligence Shenzhen Guangdong Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou Fujian Peoples R China

The electrification of public transportation is one significant step towards the sustainability of modern society. Both power networks and traffic networks are influenced by the optimal allocation of electric-bus (e-bus) charging stations. In this paper, the optimal operation cost of e-bus charging stations is first designed based on the charging price and congestion toll. Charging price and congestion toll are used to guide charging behavior to reduce total traveling and waiting time. Second, the e-bus charging station allocation method is proposed. The planning costs are optimized to locate e-bus charging stations on coupled power and traffic networks. Third, a non-dominated sorting genetic algorithm-ii (NSGA-ii) is adopted to solve the multi-objective optimization problem, which maximizes the captured traffic flow and minimizes infrastructure investment at the same time. The effectiveness of NSGA-ii is proved compared with the enumeration method. Finally, based on a 33-node power distribution network and a 77-node traffic network, simulation results demonstrate the feasibility of the e-bus charging station allocation method.

关键词： NSGA-ii distribution networks electric vehicle charging optimal operation cost Optimisation techniques Distribution networks nondominated sorting genetic algorithm-ii public transport power networks power distribution network Automobile electronics and electrics pricing captured traffic flow public transportation coupled power e-bus charging station allocation method optimal allocation traffic networks electric-bus charging stations multiobjective optimization planning genetic algorithms battery powered vehicles modern society minimizes infrastructure investment

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