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multi-objective dragonfly algorithm for optimizing a sustainable supply chain under resource sharing conditions

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Golmohammadi, Amir-Mohammad Abedsoltan, Hamidreza Goli, Alireza Ali, Irfan Arak Univ Dept Ind Engn Arak Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan Iran Aligarh Muslim Univ Dept Stat & Operat Res Aligarh 202002 India

This research addresses optimizing production, inventory, location, and routing in a multi-level supply chain. In the studied supply chain, products are first sent to several distribution centers (DC) after production. Next, these products are delivered to several companies. Companies are divided into two independent groups considering sharing logistics resources. In this regard, vehicle routing optimization is applied for each group. For this pur -pose, a mixed-integer linear programming model has been developed. This model simultaneously minimizes total supply chain costs and total negative environmental impacts. A multi-objective dragonfly algorithm (MODA) has been developed to solve this model. Then, the implementation results are compared with the Non-Dominated Sorting Genetic algorithm and Epsilon Constraint Method (EPC). Comparing the solution methods based on numerous test problems shows that the proposed MODA algorithm can be examined differently. The MODA algorithm has been able to have a significant advantage in providing solutions close to ideal points. Moreover, this algorithm has provided the most effective Pareto solutions in different test problems. However, the NSGA-II algorithm performs better regarding the spread of non-dominated solutions.

关键词： Shared logistics Location and routing Supply chain multi-objective dragonfly algorithm NSGA-II algorithm Epsilon Constraint Method

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Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition

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CHAOS SOLITONS & FRACTALS 2022年 157卷

作者： Zhou, Yilin Wang, Jianzhou Lu, Haiyan Zhao, Weigang Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sch Comp Sci Sydney Australia Beijing Inst Technol Sch Management & Econ Beijing Peoples R China

Short-term wind power prediction has a considerable effect on improving the productivity of wind energy systems and increasing economic benefits. In recently years, various wind velocity predictive models have been designed to raise the prediction effect. However, numerous predictive systems are limited by single type, and many ordinary predictive systems ignore the advantage of optimized parameters and the significance of data preparation, which bring about the lower predictive precision. To fill this gap, in this article, a novel predictive system is come up, which is on the basis of data denoising strategy, statistical predictive systems, artificial intelligence forecasting system and multi-objective optimization strategy. After using the data denoising strategy for denoising, the reconstructed data is used for the forecasting of different sub-systems, to obtain stable forecasting results, multi-objective dragonfly algorithm is used to estimate the weight coefficient of sub-systems. To evaluate the availability of the designed predictive system, five wind velocity datasets from different wind farms are used for the purpose of a case research. According four experiments and four analyses, it can be concluded that the designed combined system has a well predictive effect in short-term wind speed prediction. And it is in favor of grid regulation and operation.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Wind speed prediction Variational mode decomposition multi-objective dragonfly algorithm Hybrid models

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Robust optimization of the continuous annealing process based on a novel multi-objective dragonfly algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 106卷 104448-104448页

作者： Tian, Huixin Yuan, Chang Li, Kun Tiangong Univ Sch Elect Engn Automat Tianjin 300387 Peoples R China Tiangong Univ Key Lab Adv Elect Engn & Energy Technol Tianjin 300387 Peoples R China Tiangong Univ Sch Econ & Management Tianjin 300387 Peoples R China

Aiming at the large fluctuations in system and low efficiency caused by disturbance factors in the continuous annealing process, we established a robust multi-objective optimization model. Finding an optimal parameter setting scheme making the strip quality and capacity utilization more stable and robust is the fundamental task. For this problem, we first proposed a new robust index that combines the standard deviation and average value for guiding the model. Second, multi-objective dragonfly algorithm based on Adventure Circuitous (ACMODA) is employed to solve the model. To improve the search efficiency of the algorithm, an Adventure Circuitous strategy is adopted. The strategy enables the individuals to search for new breakthroughs basing on the weight cross-combination hybrid flight pattern when disturbance intense. The experimental results basing on the test functions show that the proposed index can evaluate the robustness precisely and ACMODA can effectively improve the convergence performance comparing with others. Experimental results basing on actual industrial problems show that ACMODA can effectively solve the robust optimization problem in the continuous annealing process.

关键词： Continuous annealing process Robust optimization multi-objective dragonfly algorithm Adventure Circuitous

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Cooperative optimal operation of hybrid energy integrated system considering multi-objective dragonfly algorithm

ENERGY STORAGE

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ENERGY STORAGE 2024年第1期6卷

作者： Gope, Sadhan Roy, Rakesh Sharma, Sharmistha Dawn, Subhojit Reddy, Galiveeti Hemakumar NIT Agartala Dept Elect Engn Agartala India NIT Meghalaya Dept Elect Engn Shillong India Velagapudi Ramakrishna Siddhartha Engn Coll Dept Elect & Elect Engn Vijayawada India Inst Technol & Management Gurugram Dept Elect Engn Gurgaon India

To meet the recent energy demand, hybrid energy systems (HESs) play an important role in providing more stable power as well as backup power to the grid. It is always preferable for the power system network to be operated in a secure, dependable, stable, and cost-effective manner. The optimal power flow (OPF) problem is used to determine the ideal power system network control parameter settings. The majority of the OPF problem, fuel cost reduction is treated as an objective function, but the environmental impact of the generating is ignored. Both fuel expense and emission are treated as objective functions of the work in this case. This research describes a solution for multi-objective optimal power flow (MOOPF) with a HES integrated conventional power system. HES in this work is made up of a wind park and a compressed air energy storage system (CAES). In this case, locational marginal pricing (LMP) is employed to determine the best location of HES in the power system. To achieve the goals of this work, the multi-objective dragonfly algorithm (MODA) is used. The IEEE 30 bus system is used to evaluate the MODA. The MODA method findings are validated against other well-known optimization algorithms such as the multi-objective ant colony system (MOACS) algorithm, multi-objective enhanced self-adaptive differential evolution (MOESDE) algorithm, modified multi-objective evolutionary algorithm-based decomposition (MOEA/D), multi-objective particle swarm algorithm (MOPOSO), and non-dominated sorting genetic algorithm (NSGA-II). image

关键词： compressed air energy storage system hybrid energy system multi-objective dragonfly algorithm

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multi-objective unit and load commitment in smart homes considering uncertainties

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第11期30卷 1-21页

作者： Alilou, Masoud Tousi, Behrouz Shayeghi, Hossein Urmia Univ Dept Elect Engn Orumiyeh Iran Univ MohagheghArdabili Energy Management Res Ctr Ardebil Iran

Although smart homes play an important role in implementing demand-side management programs, the concept is currently faced with challenges in hourly scheduling of devices, because it is difficult for consumers to manually control appliances based on time-varying prices and the power produced by local energy sources. Thus, the operating schedule of home appliances, renewable energy sources, and electric vehicles is optimized using a home energy management system in this paper. A price-based demand response program is utilized for operational scheduling. Renewable energy resources and electric vehicle uncertainties as well as the hourly variation of solar irradiance and wind power are also taken into account. To achieve the best plan of devices operation, a technical-economic formulation is developed for HEM problem, and the multi-objective dragonfly algorithm is used for its optimization. The decision-aid approach is adopted for choosing the best compromise solution, that is, selecting the best operational schedule of home appliances from the Pareto front. Numerical simulations illustrated the proper performance of the proposed method in finding the best schedule of home devices and improving the operation indices of the smart home.

关键词： electric vehicle energy management multi-objective dragonfly algorithm price-based demand response renewable energy resources smart home

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A Brawny hybrid FOFPID in MODA controller in HEVs

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2020年第7期42卷 864-881页

作者： Jadhav, Seema Haribhau Sarwadnya, R. V. NDMVP Samajs KB Thakare Coll Engn Nasik India SGGSIE& T Nanded Maharashtra India

Control system is the vital integrant in proportional-integral-derivative (PID) controller. The controllers are also planted in many special-purpose control systems. PID controller is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. In the existing process, PID controller controls water level, speed, temperature, etc. Some of the issues arise while using PID controller can be easily solved by integrating fuzzy or with some other soft computing techniques. The major difficulty in PID controller is feedback system, with constant parameters and no direct knowledge of the process. Our proposed methodology has been introduced to control the speed of highly nonlinear hybrid electric vehicles (HEVs) having an electronic throttle control system in the cascade control loop. In recent PID controllers, no such specific tuning methods are applicable. The proposed method uses fractional order fuzzy PID nonlinear controller that is used in the cascade control loop. Aging leader and challenger-aided multi-objective dragonfly algorithm is considered for controller optimization in closed loop. It is employed to enhance the gain of the controller for the reduction of integral absolute error, maximum overshoot, settling time elimination of disturbance, uncertainty model, and control HEVs.

关键词： Feedback system highly nonlinear hybrid electric vehicle (HEV) FOFPID controller multi-objective dragonfly algorithm aging leader and challenger

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Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles

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SOLAR ENERGY 2020年 212卷 6-18页

作者： Alilou, Masoud Tousi, Behrouz Shayeghi, Hossein Urmia Univ Dept Elect Engn Orumiyeh Iran Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran

Implementing demand side management programs in a residential area causes to increase the role of consumers in managing the total power network. Moreover, the owner of the smart home can reduce energy dependence on the power network and also his electricity bill by using optimal managing the operational schedule of home appliances and available generated power of renewable distributed generation and electric vehicle. In this paper, a new multi-objective scheduling method based on intelligent algorithms is utilized for energy managing in smart homes of a residential micro grid. Home appliances, rooftop photovoltaic panel and plug-in hybrid electric vehicle are schedulable devices of each smart home. Photovoltaic and electric vehicle uncertainties are also considered. The combination algorithm of the multi-objective dragonfly algorithm and analytical hierarchy process method is used for optimizing the techno-economic objective function and finding the best schedule of devices. Real-time pricing tariff is considered as the price-based demand response program. For evaluating the efficiency of the proposed method, it is applied to a smart micro grid with 20-smart home. The numerical result demonstrates the appropriate performance of the proposed home energy management method in reducing the electricity bill of smart homes and peak demand of the residential smart micro grid.

关键词： Electricity bill Latin hypercube sampling method Smart grid multi-objective dragonfly algorithm Rooftop photovoltaic panel Smart home energy management

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multi-objective energy management of smart homes considering uncertainty in wind power forecasting

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ELECTRICAL ENGINEERING 2021年第3期103卷 1367-1383页

作者： Alilou, Masoud Tousi, Behrouz Shayeghi, Hossein Urmia Univ Dept Elect Engn Orumiyeh Iran Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran

Smart homes are one of the effective parts of a smart grid. A residential smart grid operates properly when each of smart homes is managed appropriately. The energy management system of a home has to simultaneously manage the operational time of appliances and how to use the energy of an electric vehicle and home renewable distributed generation units like a wind turbine. So, the energy management of smart homes in a residential smart grid is studied in this paper. The wind turbine is considered as the energy source of the smart home while the electric vehicle can operate as a producer/consumer unit in discharge/charge mode. The uncertainty model of wind turbine and electric vehicles are also considered in the proposed method for energy managing. The real-time pricing tariff is utilized for applying the demand response program. The electricity cost of consumers and the peak to average rate of demand are formulated to achieve the best schedule of devices of smart homes. multi-objective dragonfly algorithm is utilized for optimizing the technical and economic objective functions. After finding the optimal Pareto front, the analytical hierarchy process method is used to select the best operational schedule of smart homes. The proposed method is evaluated in a sample smart grid. Numerical results show that the proposed management method has considerable efficiency in improving the performance of the smart grid.

关键词： Analytical hierarchy process Electric vehicle Home energy management multi-objective dragonfly algorithm Smart grid Wind turbine

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Optimization of Modern Manufacturing Processes Using Three multi-objective Evolutionary algorithms: A Step Towards Selecting Efficient algorithms

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2021年第3期12卷 96-124页

作者： Majumder, Arindam Natl Inst Technol Dept Mech Engn Agartala India

The optimization in manufacturing processes refers to the investigation of multiple responses simultaneously. Therefore, it becomes very necessary to introduce a technique that can solve the multiple response optimization problem efficiently. In this study, an attempt has been taken to find the application of three newly introduced multi-objective evolutionary algorithms, namely multi-objective dragonfly algorithm (MODA), multi-objective particle swarm optimization algorithm (MOPSO), and multi-objective teaching-learning-based optimization (MOTLBO), in the modern manufacturing processes. For this purpose, these algorithms are used to solve five instances of modern manufacturing process-CNC process, continuous drive friction welding process, EDM process, injection molding process, and friction stir welding process-during this study. The performance of these algorithms is measured using three parameters, namely coverage to two sets, spacing, and CPU time. The obtained experimental results initially reveal that MODA, MOPSO, and MOTLBO provide better solutions as compared to widely used nondominated sorting genetic algorithm II (NSGA-II). Moreover, this study also shows the superiority of MODA over MOPSO and MOTLBO while considering coverage to two sets and CPU time. Further, in terms of spacing a marginally inferior performance is observed in MODA as compared to MOPSO and MOTLBO.

关键词： Evolutionary algorithm Modern Manufacturing Process multi-objective dragonfly algorithm multi-objective Optimization multi-objective Particle Swarm Optimization algorithm

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multi-objective optimization of multi-item EOQ model with partial backordering and defective batches and stochastic constraints using MOWCA and MOGWO

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OPERATIONAL RESEARCH 2020年第3期20卷 1729-1761页

作者： Khalilpourazari, Soheyl Pasandideh, Seyed Hamid Reza Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

In this paper a multi-objective mathematical model is proposed for multi-item EOQ model considering partial backordering and defective supply batches. In order to consider real world situations, different stochastic operational constraints are considered and implemented under uncertainty. For the first time in this research, the rate of partial backordering is considered as decision variable. The aim is to determine time interval between successive supply deliveries, rate of partial backordering and filling rate from stock in order to minimize total inventory costs including holding, backordering and ordering costs, while, minimizing required warehouse space. Due to complexity and nonlinearity of the proposed mathematical model from one hand and importance of providing the decision maker with efficient Pareto optimal solutions, five meta heuristic algorithms as well as one hybrid exact solution method are utilized to solve the problem. To determine the most efficient solution method, the performance of the algorithms is investigated through a deep computational analysis considering different measures including diversity, number of Pareto optimal solutions, spacing and computation time. In addition, single factor ANOVA is utilized to determine significant difference among algorithms at ninety-five percent confidence level to demonstrate the superior algorithm.

关键词： EOQ Partial backordering Defective supply batches multi-objective Grey Wolf Optimizer multi-objective Water Cycle algorithm multi-objective dragonfly algorithm

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