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A multi objective-BSC model for new product development project portfolio selection

EXPERT SYSTEMS WITH APPLICATIONS 2020年 162卷 113757-113757页

作者： Abbasi, Darya Ashrafi, Maryam Ghodsypour, Seyed Hassan Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

The most significant factor for the survival of an enterprise under a high level of competition is new product development (NPD). As a result, selecting a potential NPD project portfolio to gain competitive advantage has become a major concern to enterprises. However, selection of an NPD project portfolio is intricate due to multiple selection criteria and factors. This study focuses on optimizing an NPD project portfolio selection problem. To this end, Balance Score Card (BSC) is employed as a comprehensive framework to define NPD project selection criteria. Afterward, a multi-objective mathematical model is formulated that attempts to maximize total outcome, to minimize total risk, and to maximize strategic advantages. Our proposed model also takes into account suppliers, consumer demands, and project interdependencies. Because of the NP-hardness of the proposed model, two multi-objective metaheuristic algorithms, multi-objective particle swarm optimization (MOPSO), and non dominated sorting genetic algorithm (NSGA-II) are applied to solve the proposed model. It should be noted that the performance of algorithms is evaluated using the epsilon-constraint method and enhanced using response surface methodology (RSM). Finally, several numerical examples of different sizes are generated to compare the performance of metaheuristic solution methods based on four comparing metrics. Computational results show that NSGA-II outperforms MOPSO in terms of all the evaluation metrics. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： New product development Project portfolio selection multi-objective optimization problems Non dominated sorting genetic algorithm multi-objective particle swarm optimization epsilon-Constraint method

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multiple swarms multi-objective particle swarm optimization Based on Decomposition

Multiple Swarms Multi-objective Particle Swarm Optimization ...

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Peng Hu Li Rong Cao Liang-lin Li Li-xian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China

particle swarm optimization is a very competitive swarm intelligence algorithm for multi-objective optimization problems, but because of it is easy to fall into local optimum solution, and the convergence and accuracy of Pareto solution set is not satisfactory. So we proposed a multi-swarm multi-objective particle swarm optimization based on decomposition (MOPSO_MS), in the algorithm each sub-swarm corresponding to a sub-problem which decomposed by multi-objective decomposition method, and we constructed a new updates strategy for the velocity. Finally, through simulation experiments and compare with the state-of-the-art multi-objective particle swarm algorithm on ZDT test function proved the convergence and the accuracy of the algorithm. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： multi-objective particle swarm optimization multi-swarm particle swarm optimization multi-objective decomposition Performance indicator

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Planning and Impact Evaluation of Distributed Generators in Indian Context using multi-objective particle swarm optimization

Planning and Impact Evaluation of Distributed Generators in ...

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Jain, Naveen Singh, S. N. Srivastava, S. C. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781457710018

The placement and sizing of Distributed Generators (DGs) can be formulated as a nonlinear optimization problem to maximize the benefits from its placement, while minimizing its size. This paper proposes a voltage stability index based method for the DG placement and a meta-heuristic technique based on multi-objective particle swarm optimization (MOPSO) to provide Pareto optimal solutions. The MOPSO minimizes the DG size as well as the weighted sum of multiple system indices, reflecting the impact of DGs on the improvement of the system performance. The Pareto-optimal solutions present the possible trade-off between the multi-objective index and the DG size. The studies have been carried out on a 30-bus test system and a 41-bus Indian distribution system. The impacts of the DG placement on the system voltage profile, line loss, environment and cost of generation have also been investigated.

关键词： Distributed generation multi-objective particle swarm optimization radial distribution system

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Traffic Flow Guidance and optimization of Connected Vehicles Based on swarm Intelligence 38

Traffic Flow Guidance and Optimization of Connected Vehicles...

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38th Chinese Control Conference (CCC)

作者： Li, Qiuyan Wei Shangguan Cai, Baigen Chai, Linguo Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

ISBN: (纸本)9789881563972

In order to solve the problem of traffic congestion and frequent accidents caused by economic, social and urban development, this paper proposes an optimization method for traffic flow guidance of connected vehicles based on vehicle-road collaborative advanced technology. In the paths between the source and destination, road resources are allocated according to the capacity and saturation constraints of the path, an adaptive multi-objective particle swarm optimization algorithm is proposed. A multi-objective optimization model of traffic flow guidance based on swarm intelligence is established. Finally, simulation results show that the proposed optimization method reduces road saturation, travel time and delay by 5.03%, 1.04% and 25.44% respectively compared with the traditional route selection method, which shows that it can improve traffic efficiency and make efficient use of road resources.

关键词： Intelligent transportation swarm intelligence multi-objective particle swarm optimization traffic flow guidance road capacity

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Hybrid Solar Power System optimization based on multi-objective PSO Algorithm

Hybrid Solar Power System Optimization based on Multi-Object...

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Chinese Automation Congress (CAC)

作者： Wang, Xiao Duan, Yanglong Xiao, Bin Zhou, Zhi Peng, Huaiwu Wang, Ying POWERCHINA Northwest Engn Corp Ltd Dept New Energy Project Xian Peoples R China Xi An Jiao Tong Univ Dept Elect & Informat Engn Xian Peoples R China

ISBN: (纸本)9781728140940

Nowadays, with the rapid development of related technologies, the solar power become an important component of the whole energy system. At present, there are two main forms of solar power generation: photovoltaics (PV) and concentrated solar power (CSP). Among them, PV is much cheaper, but more susceptible to resource conditions, while CSP is more controllable based on the thermal storage section, but still expensive. To integrate the advantages of these two kinds of technologies, the hybrid solar power system is proposed by many researchers. Some single objective optimization algorithms are used in the process of design such systems. However, it is still a dilemma to deal with the cost and stability in the hybrid system. In this paper, we try to propose an multi-objective particle swarm optimization (MO_PSO) algorithm to solve this problem, which can consider the performance and cost of the project at the same time. The experimental result based on the real data shown that this algorithm can provide a feasible solution of the hybrid power system with stable output and acceptable cost. Furthermore, this method based on artificial intelligence can be used in other hybrid systems optimization in the smart grid.

关键词： Photovoltaic Concentrated Solar Power Hybrid System Solar energy multi-objective particle swarm optimization

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Modeling and optimization of the Activated Sludge Process 38

Modeling and Optimization of the Activated Sludge Process

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38th Chinese Control Conference (CCC)

作者： Huang, Shun Zhang, Lijun Guo, Hui Chen, Peng Xia, Wei Hu, Cong China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China

ISBN: (纸本)9789881563972

In the process of urban sewage treating, reducing the energy consumption and improving the quality of the effluent are significantly meaningful. According to the activated sludge method, the key factors affecting the energy consumption and water quality of wastewater treatment are determined. In order to minimize the energy consumption of the activated sludge process and maximize the quality of the effluent, four different objective functions are modeled [i.e., the airflow rate, the carbonaceous biochemical oxygen demand (CBOD) of the effluent, the total phosphorus (TP) of the effluent, and the ammonia nitrogen of the effluent (NH4-N)]. These models are developed using a back propagation (BP) neural network based on industrial data, and dissolved oxygen (DO) is the controlled variable. A multi-objective model was evaluated by six evaluation indicators. Based on the analysis of the model and the mechanism of activated sludge process, the multi-objective particle swarm optimization(MOPSO) algorithm was used to optimize the energy consumption and water quality of the activated sludge process. The experimental results show that eventually reduce aeration energy consumption by 17%.

关键词： Activated sludge Wastewater treatment Energy consumption Back propagation neural network multi-objective particle swarm optimization

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A numerical study on optimal FTMD parameters considering soil-structure interaction effects

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GEOMECHANICS AND ENGINEERING 2018年第5期16卷 527-538页

作者： Etedali, Sadegh Seifi, Mohammad Akbari, Morteza Birjand Univ Technol Dept Civil Engn POB 97175-569 Birjand Iran

The study on the performance of the nonlinear friction tuned mass dampers (FTMD) for the mitigation of the seismic responses of the structures is a topic that still inspires the efforts of researchers. The present paper aims to carry out a numerical study on the optimum tuning of TMD and FTMD parameters using a multi-objective particle swarm optimization (MOPSO) algorithm including soil-structure interaction (SSI) effects for seismic applications. Considering a 3-story structure, the performances of the optimized TMD and FTMD are compared with the uncontrolled structure for three types of soils and the fixed base state. The simulation results indicate that, unlike TMDs, optimum tuning of FTMD parameters for a large preselected mass ratio may not provide a best and optimum design. For low mass ratios, optimal selection of friction coefficient has an important key to enhance the performance of FTMDs. Consequently, a free parameter search of all FTMD parameters provides a better performance in comparison with considering a preselected mass ratio for FTMD in the optimum design stage of the FTMD. Furthermore, the SSI significant effects on the optimum design of the TMD and FTMD. The simulation results also show that the FTMD provides a better performance in reducing the maximum top floor displacement and acceleration of the building in different soil types. Moreover, the performance of the TMD and FTMD decrease with increasing soil softness, so that ignoring the SSI effects in the design process may give an incorrect and unrealistic estimation of their performance.

关键词： seismic-excited structures tuned mass damper friction tuned mass damper multi-objective particle swarm optimization soil-structure interaction

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multi-objective Optimal Predictive Energy Management Control of Grid-Connected Residential Wind-PV-FC-Battery Powered Charging Station for Plug-in Electric Vehicle

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2018年第2期13卷 742-751页

作者： El-naggar, Mohammed Fathy Elgammal, Adel Abdelaziz Abdelghany UTT Dept Energy SMIEEE MIET Arima Trinidad Tobago Prince Sattam bin Abdulaziz Univ Al Kharj Dept Elect Engn Al Kharj Saudi Arabia Helwan Univ Dept Elect Engn Helwan Egypt

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using multi-objective particle swarm optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

关键词： Plug-in Electric Vehicles (PEVs) Predicative control Wind-PV-FC battery charging station Smart grid Grid to Vehicle (G2V) Energy management multi-objective particle swarm optimization

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Power System Transient Stability: An Algorithm for Assessment and Enhancement Based on Catastrophe Theory and FACTS Devices

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IEEE ACCESS 2018年 6卷 26424-26437页

作者： Eladany, Mai Mahmoud Eldesouky, Azza Ahmad Sallam, Abdelhay Ahmed Port Said Univ Elect Power Dept Port Fouad 42526 Egypt

It is of crucial importance to obviate power system damage and cascading failures that may cause a full or partial blackout when the system is exposed to severe contingencies. Flexible alternating current transmission system (FACTS) devices have been harnessed for solving several power system problems including transient stability. Ever since, to emphasize the effectiveness of the FACTS technology, the number and allocation of these devices must be selected properly. So, a novel algorithm is proposed in this paper to determine the best least number (BLN) and allocation of the thyristor-controlled series capacitor (TCSC) with a goal of improving the transient stability in an optimal manner. A combination of the catastrophe theory (CT) and the multi-objective particle swarm optimization (MOPSO) method in addition to a clustering technique is used to structure the proposed algorithm. The CT is used to assess the transient stability and calculate the critical clearing time (CCT). MOPSO is applied to compromise between maximizing the CCT and minimizing the cost of TCSCs as two contradictory objective functions. The clustering technique is designed to provide the BLN of TCSC devices. Accordingly, at least investment, the proposed algorithm satisfies an increase of the stability margin by increasing the value of CCT for each generator and improves the location of operating points in the CT's stability region. Simulation of the proposed algorithm application to New England 39-bus power system is presented to verify the algorithm effectiveness. The results confirm the feasibility of this algorithm and are validated in comparison with those obtained through time-domain simulation.

关键词： Catastrophe theory FACTS devices multi-objective particle swarm optimization transient stability

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Parameter optimization design of vehicle E-HHPS system based on an improved MOPSO algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2018年 123卷 51-61页

作者： Zhao Wanzhong Luan Zhongkai Wang Chunyan Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Jiangsu Peoples R China

To improve the handling stability as well as reduce the steering energy consumption of heavy commercial vehicle, a novel electric-hydraulic hybrid power steering (E-HHPS) system with multiple steering modes is presented, which enables the vehicle to acquire the steering handiness at low speed and better steering road feeling at high speed by switching the actuator unit according to the current working condition. In this paper, to achieve the design goals of E-HHPS system, which are to reduce steering energy consumption and improve steering stability, three evaluation indexes of E-HHPS system are established, which convert the E-HHPS system parameter optimization problem into a multi-objective optimization model. Because it is difficult to approximate the Pareto front of the transformed optimization model by basic algorithms, a multi-objective particle swarm optimization algorithm based on adaptive decomposition (MOPSO/AD) is proposed. Test functions are used to verify the performance of the algorithm and test results show that the MOPSO/AD algorithm has better comprehensive performance and stability compared with the basic MOPSO algorithm and MOEA/D algorithm. The MOPSO/AD algorithm is applied to solve the E-HHPS system optimization model and simulation results show that the proposed MOPSO/AD algorithm has better convergence in solving the E-HHPS parameter optimization problem compared with MOPSO, which enables the optimized E-HHPS system has good handling stability and low steering energy consumption.

关键词： Electric-hydraulic hybrid steering parameter optimization multi-objective particle swarm optimization decomposition method

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