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water wave optimization algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

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IEEE ACCESS 2024年 12卷 48027-48043页

作者： Huynh, Duy C. Ho, Loc D. Pham, Hieu M. Dunnigan, Matthew W. Barbalata, Corina HUTECH Univ Dept Elect Engn Ho Chi Minh City 70000 Vietnam Heriot Watt Univ Dept Elect Engn Edinburgh EH14 4AS Scotland Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response todynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-aheadload forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of populationsize, parameter tuning, exploitation and exploration, convergence speed, as well as optimization *** leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is appliedto the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solvedby day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs,ensuring optimal decision-making in the face of varying load scenarios. With the competition of various day-ahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows itsadvantage features, being more transparent, more effective, and more robust than other techniques, especiallytransparent explainability, as well as simple and fast in model training. These are requirements to achievethe result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load inthe microgrid in this paper. The simulation results show that the percentage error (PE) between the MLRmodel-based forecasted and actual load powers is always less than 4.42%, the mean absolute percentageerror (MAPE) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievementsmeet the accurate and fast requirements. They are completely competitive with the results of using othertechniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especiallyin the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch inthe microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are68.76 ($), 5.09 ($), and

关键词： Dynamic optimal dispatch day-ahead load forecasting microgrid water wave optimization algorithm

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An ensemble discrete water wave optimization algorithm for the blocking flow-shop scheduling problem with makespan criterion

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APPLIED INTELLIGENCE 2022年第14期52卷 15824-15843页

作者： Zhao, Fuqing Shao, Dongqu Xu, Tianpeng Zhu, Ningning Jonrinaldi Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Univ Andalas Dept Ind Engn Padang 25163 Indonesia

Production scheduling plays a pivotal role in smart factories due to the development of intelligent manufacturing. As a typical scheduling problem, the blocking flow-shop scheduling problem (BFSP) has attracted enormous attention from researchers. In this paper, an ensemble discrete water wave optimization algorithm (EDWWO) is proposed with the criterion to minimize the makespan. In the proposed algorithm, a constructive heuristic is presented to suit the needs of initial solutions quality. The constructive heuristic is based on a new dispatching rule combined with the well-known NEH heuristic. The algorithmic characteristics are explored and effective technologies, such as data-driven mechanism in the propagation phase, a block-shifting operator based on the framework of the variable neighborhood search in the breaking phase, and perturbation strategy, are employed to improve the performance of the algorithm. The effectiveness of operators and parameters in EDWWO are analyzed and calibrated based on the design of experiments. To evaluate the algorithmic performance, the well-known benchmark problem is adopted for comparison with five other state-of-the-art algorithms. Meanwhile, the statistical validity of the results is investigated by introducing the Friedman-test and Wilcoxon-test. The statistical results demonstrate the effectiveness of EDWWO for solving the BFSP.

关键词： Production scheduling Blocking flow-shop scheduling problem water wave optimization algorithm Makespan Data-driven mechanism

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A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems

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MATERIALS TESTING 2021年第6期63卷 560-564页

作者： Gurses, Dildar Pholdee, Nantiwat Bureerat, Sujin Sait, Sadiq M. Yildiz, Ali Riza Uludag Univ Bursa Turkey Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Bursa Uludag Univ Dept Automot Engn Bursa Turkey

In this work, a new hybrid optimization algorithm (HWW-NM), which combines the Nelder-Mead local search algorithm with the water wave algorithm, is introduced to solve real-world engineering optimization problems. This paper is one of the first studies in which both the water wave algorithm and the HWWNM are applied to processing parameters optimization for manufacturing processes. HWW-NM performance is measured using the cantilever beam problem. Additionally, a problem for milling manufacturing optimization is posed and solved to evaluate HWW-NM performance in real-world applications. The results reveal that HWW-NM is an attractive optimization approach for optimizing real-life problems.

关键词： water wave optimization algorithm Nelder-mead manufacturing design milling

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A two-stage discrete water wave optimization algorithm for the flowshop lot-streaming scheduling problem with intermingling and variable lot sizes

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KNOWLEDGE-BASED SYSTEMS 2022年 238卷 1页

作者： Wang, Wenyan Xu, Zhenhao Gu, Xingsheng East China Univ Sci & Technol Coll Informat Sci & Engn Shanghai 200237 Peoples R China

Considering lot-streaming of different orders in the actual production system, this paper examines the lot-streaming scheduling problem in flowshop production system. Given production sublots of the different jobs allowed to be intermingled in the production process, we take a deeper dive into the flowshop lot-streaming scheduling problem with detached setup and transfer batches for production sublots of jobs. And the mathematical model is presented with the objective of makespan. To tackle the complexity of the problem at hand, a two-stage discrete water wave optimization (TSDWWO) algorithm is proposed to optimize the sublots and sequencing. In the algorithm, a double-wave parallel encoding scheme is designed for two kinds of water waves, which are splitting wave and sequencing wave. Besides, considering the operation effect and time of the algorithm, the corresponding operations during the process of wave propagation are improved for these two kinds of waves respectively. Afterward, the parameters of the proposed TSDWWO are calibrated by the design of experiments approach. To evaluate the performance of the proposed TSDWWO, two different types of test sets are utilized in the experiments, which are the test sets in the literature and test sets randomly generated containing instances of variant scales. Compared with other existing algorithms in the literature, the results show the efficiency of this approach for the flowshop lot-streaming scheduling problem. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Lot-streaming Flowshop Intermingling Batch transfer water wave optimization algorithm

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Optimal reactive power dispatch using water wave optimization algorithm

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OPERATIONAL RESEARCH 2020年第4期20卷 2537-2553页

作者： Zhou, Yongquan Zhang, Jinzhong Yang, Xiao Ling, Ying Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China

This paper presents water wave optimization (WWO) algorithm to solve the optimal reactive power dispatch (ORPD) problem with the continuous and discrete control variables in power system. The ORPD problem is defined as a complex, discrete, constrained nonlinear combinatorial optimization problem. The WWO algorithm is utilized to find the optimized values of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices to achieve minimized value of active power losses. The WWO algorithm not only effectively avoids the shortcomings of local search and poor calculation accuracy, but also accelerates the convergence rate to find the global optimal solution. The WWO algorithm is implemented on standard IEEE 30-bus power system that is to verify the effectiveness and feasibility of the WWO algorithm to tackle with the ORPD problem. Compared with other algorithms, the WWO algorithm can find the set of the optimal solutions of control variables. The simulation experiment indicates that the WWO algorithm has better overall performance to reduce the real power losses.

关键词： water wave optimization algorithm Optimal reactive power dispatch Control variables Active power losses Simulation experiment

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A new water wave optimization algorithm for satellite stability

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CHAOS SOLITONS & FRACTALS 2020年 138卷 109793-109793页

作者： Jin, Yang Li, Shuai Ren, Lu Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Inst Spacecraft Syst Engn Beijing 100094 Peoples R China

Scientific and efficient design is the foundation requirement for stable operation of satellites. While a satellite is composed of a number of subsystems with independent functions. These subsystems interact with each other and related design variables are also coupled with each other. Thus, the overall design process needs to comprehensively weigh the relationship among multiple design objectives and design constraints of different subsystems, in order to obtain the optimal balance of the overall framework. Here we propose a novel Powell-water wave optimization (POWWO) algorithm and apply it to the overall optimization design of earth observation satellites. Compared with previous works, this algorithm shows more excellent optimization properties, via combining the global and local search ability. Its effectiveness can also be supported by the well-known benchmarks and satellite optimization design experiments. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： water wave optimization algorithm optimization design

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A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 153卷 107082-107082页

作者： Zhao, Fuqing Zhang, Lixin Cao, Jie Tang, Jianxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China

The distributed assembly no-idle flow-shop scheduling problem (DANIFSP) is a novel and considerable model, which is suitable for the modern supply chains and manufacturing systems. In this study, a cooperative water wave optimization algorithm, named CWWO, is proposed to address the DANIFSP with the goal of minimizing the maximum assembly completion time. In the propagation phase, a reinforcement learning mechanism based on the framework of the VNS is designed to balance the exploration and exploitation of the CWWO algorithm. Afterwards, the path-relinking combined with the VNS method as the modified breaking operator is introduced to enhance the capability of local search. Furthermore, a multi-neighborhood perturbation strategy in the refraction phase is applied to extract knowledge information to increase the probability of escaping the local optimal. Moreover, the comprehensive experimental program is executed to calibrate the control parameters of the CWWO algorithm and illustrate the cooperative effect of the three modified operations. The performance of the CWWO algorithm is verified on the benchmark set, and the experimental results demonstrated the stability and validity of the CWWO algorithm.

关键词： water wave optimization algorithm Distributed assembly no-idle flow-shop scheduling problem Reinforcement learning Variable neighborhood search

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Submerged Arches Optimal Design With a Multi-Method Ensemble Meta-Heuristic Approach

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IEEE ACCESS 2020年 8卷 215057-215072页

作者： Perez-Aracil, Jorge Camacho-Gomez, Carlos Hernandez-Diaz, Alejandro Manuel Pereira, Emiliano Salcedo-Sanz, Sancho Catholic Univ Murcia UCAM Civil Engn Dept Murcia 30107 Spain Univ Politecn Madrid Dept Comp Syst Engn Madrid 28040 Spain Univ La Laguna Continuum Mech & Struct Anal Santa Cruz De Tenerife 38205 Spain Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28871 Spain

This paper discusses the performance of a multi-method ensemble meta-heuristic for optimization in a problem of submerged arches design. Specifically, the Coral Reefs optimization algorithm with Substrate Layers (CRO-SL) is proposed. It is a multi-method evolutionary ensemble which combines different searching structures in a single population. In this case, some novel searching strategies such as the Firefly algorithm and the water wave optimization have been proposed to be included in the CRO-SL algorithm. In addition, some other traditional searching structures such as classical crossovers, Gaussian mutation operators, Harmony search and Differential Evolution search strategies have also been implemented in the algorithm. The proposed CRO-SL has been applied to the optimal design of submerged arches in deep waters, considering several design aspects such as the bending moment of the beam and the airspace of the arch. The evaluation of the submerged arches quality is obtained after the simulation of the arches by means of a finite elements computational software, which has been hybridized with the CRO-SL algorithm in the process of fitness calculation. It will be shown that the CRO-SL is able to obtain excellent solutions to submerged arches design problems. For this, the computational performance of each substrate (different searching strategies) in this optimization problem will be discussed and the results obtained will be compared with those in alternative works for similar experiments.

关键词： optimization Shape Bending Substrates Search problems Signal processing algorithms Heuristic algorithms Submerged arches design ensemble methods evolutionary algorithms firefly algorithm water wave optimization algorithm

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Optimal energy management and spinning reservation system in independent microgrids considering practical constraints and demand response

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SUSTAINABLE CITIES AND SOCIETY 2023年 92卷

作者： Weixing, Chen Yan, Feng Bodong, Song Ghaemi, Noura North Sichuan Med Coll Nanchong 637007 Peoples R China Hanyang Univ Seoul 02581 South Korea Macao Polytech Univ Macau 999078 Peoples R China Islamic Azad Univ Fac Elect & Comp Engn Ardebil Iran

Programming for supplying electricity in these regions is performed by developing the global electricity system and using islanded microgrids (MGs). Herein, a probabilistic model for simultaneous programming of energy and reserve of an islanded MG is proposed by considering demand-response (DR) programs and security constraints using a developed water wave search algorithm. Since the above problem has many nonlinear constraints, in order to avoid early convergence and not be located in local points, an additional operator is provided to improve the search performance in the feasible space of the problem. In the proposed method, the weak waves are eliminated, and the waves that are close to the optimal answer are strengthened;so that the unworthy answers are removed from the population and worthy answers are created in their place. The objective function aims to maximize the MG operator's profit by considering several security-related and operational constraints, e.g., voltage and frequencies. In the proposed method, the conditional value at risk (CVaR) method is adopted to evaluate and manage the risk of problem uncertainties. An effort is made to coordinate the variables of the problem, such that their generation capacity can be determined based on the created scenarios. In this method, the operator's profit sensitivity and the safety margins of the MG with and without the participation of responsive loads are evaluated concerning the risk index. This method enables the operators to select the proper risk coefficient, maximize their profit and improve the safety margin, voltage, and frequency of the MG. Finally, the proposed method and model have been discussed and investigated on the studied system on different sce-narios. The numerical results show that with the participation of customers in the DR program, the expected profit of the operator and the security margin of voltage and frequency increase. In periods of low load, pro-duction units have more free capaci

关键词： Economic programming Energy and reserve scheduling Demand-side management Risk system Probabilistic renewable energy sources model water wave optimization algorithm

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Two efficient nature inspired meta-heuristics solving blocking hybrid flow shop manufacturing problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104196-104196页

作者： Aqil, Said Allali, Karam Univ Hassan II Casablanca Lab Math Comp Sci & Applicat FST POB 146 Mohammadia Morocco

The hybrid flow shop scheduling problem is one of the most relevant optimization problem in manufacturing industry. In this paper, we investigate the blocking hybrid flow shop scheduling problem under the constraint of sequence dependent setup time. The objective is to minimize the total tardiness and earliness with uniform parallel machines under the constraint of sequence dependent setup time. To solve this kind of problems, significant developments of new meta-heuristic algorithms make it possible to implement new metaheuristics inspired by the behavior of living beings or natural phenomena. In this context, we suggest six algorithms based on the migratory bird optimization and the water wave optimization algorithms. We give three new versions for each meta-heuristic in order to solve this optimization problem. The main improvement of the suggested algorithms concerns the exploration phase of the neighborhood system. The enhancement approaches are based on the iterated greedy algorithm, the greedy randomized adaptive search procedure, the path relinking technique and the local search procedures. These modifications in the two nature inspired meta-heuristics make it possible to develop a new neighborhood generation structure constituting hybrid optimization algorithms. A comparative study between the different proposed methods is carried out on a variety of problems ranging from small to relatively large size instances. The simulations show good performances recorded by the water wave optimization algorithm in term of quality and convergence speed towards the best solution.

关键词： Hybrid flow shop water wave optimization algorithm Migratory bird optimization algorithm Blocking Total tardiness and earliness

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