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International Conference on Electronical, Mechanical and Materials Engineering

作者： Weiguo Zhang Jinzhong Zhang School of Computer Science Xi`an University of Science and Technology

ISBN: (纸本)9781510882584

The water cycle algorithm(WCA) is a simple and effective global optimization algorithm, mainly used for engineering optimization. However, It is easy to fall into the local optimal solution when solving some constrained problems. Therefore, this paper proposes a improved water cycle algorithm based on chaotic Levy distribution. The improved water cycle algorithm was named as CLWCA, and chaos sequence was used as step control parameter based on Levy distribution in combination with the perturbation of Levy distribution and the boundedness of chaotic sequence, in order to enhance the global searching ability of the algorithm. In this paper, CLWCA is applied in the field of engineering model design and compared with the traditional water cycle algorithm(WCA) through experiments. The result verified that the CLWCA algorithm has better global search ability and stability than WCA algorithm in the field of industrial model design.

关键词： water cycle algorithm chaotic sequence levy distribution engineering model

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Optimal Dynamic Voltage Restorer Using water cycle Optimization algorithm

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Computer Systems Science & Engineering 2023年第4期45卷 595-623页

作者： Taweesak Thongsan Theerayuth Chatchanayuenyong Faculty of Engineering Mahasarakham UniveristyMaha Sarakham44150Thailand

This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer(DVR)in a three-phase *** control scheme employs the fractional order,proportional-integral-derivative(FOPID)controller to improve on the DVR performance in order to enhance the power quality in terms of the response time,steady-state error and total harmonic distortion(THD).The result obtained was compared with fractional order,proportionalintegral(FOPI),proportional-integral-derivative(PID)and proportional-integral(PI)controllers in order to show the effectiveness of the proposed DVR control scheme.A water cycle optimization algorithm(WCA)was utilized to find the optimal set for all the controller *** were used to solve four power quality issues;balanced voltage sag,balanced voltage swell,unbalanced voltage sag,and unbalanced voltage *** showed that one set of controller gain obtained from the WCA could solve all the power quality issues while the others in the literature needed an individual set of optimal gain for each power quality *** prove the concept,the proposed DVR algorithm was simulated in the MATLAB/Simulink software and the results revealed that the four optimal controllers can compensate for all the power quality problems.A comparative analysis of the results in various aspects of their dynamic response and%THD was discussed and *** was found that PID controller yields the most rapid performance in terms of average response time while FOPID controller yields the best performance in term of average%steady-state *** controller was found to provide the lowest THD percentage in the average%*** did not differ much in average response from the PID and average%THD from FOPI;however,FOPID provided the most outstanding average steady-state *** to the CBMA curve,the dynamic responses of all controllers fall in the acceptable power quality *** total harmonic distortion(THD)of the compensated load vol

关键词： Dynamic voltage restorer FOPID controller FOPI controller water cycle algorithm

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Hybrid water cycle Optimization algorithm With Simulated Annealing for Spam E-mail Detection

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IEEE ACCESS 2019年 7卷 143721-143734页

作者： Al-Rawashdeh, Ghada Mamat, Rabiei Abd Rahim, Noor Hafhizah Binti Univ Malaysia Terengganu Dept Comp Sci Kuala Terengganu 21300 Malaysia

Spam is defined as junk and unwanted e-mail. The implementation of a reliable spam email filter becomes more and more important for e-mail users since they have to face with the growing amount of uninvited e-mails. The faults of spam classifiers are characterized by being more and more insufficient to handle huge volumes of relevant emails and to identify and detect the new spam email as example with high performance. The problem in spam classifiers is a huge number of features. Feature selection is an important task in keyword content classification for being among the most popular and effective methods for feature reduction. Accordingly, irrelevant and redundant features that can impede performance would be eliminated. Meta-heuristic optimization is to choose the optimal solution between possible multi-solutions, which respect the aim of this research that is the performance. The other problem is related to ambiguity of the effect of optimization feature selection on multiple classifiers algorithm which are popular used by previous work namely;K-nearest Neighbor, Naive Bayesian and Support Vector Machine. Therefore, the aim of this research is to improve the accuracy of feature selection by applying hybrid water cycle and Simulated Annealing to optimize results and to evaluate the proposed Spam Detection. The methodology used in this study which consists of groundwork, induction, improvement, evaluation and comparison quality. The cross-validation was used for training and validation dataset and seven datasets were employed in testing the spam classification proposed. The results demonstrate that the meta-heuristic namely water cycle feature selection (WCFS) was employed and three ways of hybridization with Simulated Annealing as a feature selection employed. In comparison with other feature selection algorithms such as Harmony Search, Genetic algorithm, and Particle Swarm, the hybridization interleaved hybridization outperformed other feature selection algorithms

关键词： water cycle algorithm classification algorithm spam email simulating annealing hybridization global search local search

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Short Term Economic Emission Power Scheduling of Hydrothermal Energy Systems Using Improved water cycle algorithm

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MEHRAN UNIVERSITY RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY 2017年第2期36卷 255-272页

作者： Haroon, Shaikh Saaqib Malik, Tahir Nadeem Univ Engn & Technol Dept Elect Engn Taxila Pakistan

Due to the increasing environmental concerns, the demand of clean and green energy and concern of atmospheric pollution is increasing. Hence, the power utilities are forced to limit their emissions within the prescribed limits. Therefore, the minimization of fuel cost as well as exhaust gas emissions is becoming an important and challenging task in the short-term scheduling of hydro-thermal energy systems. This paper proposes a novel algorithm known as WCA-ER (water cycle algorithm with Evaporation Rate) to inspect the short term EEPSHES (Economic Emission Power Scheduling of Hydrothermal Energy Systems). WCA has its ancestries from the natural hydrologic cycle i.e. the raining process forms streams and these streams start flowing towards the rivers which finally flow towards the sea. The worth of WCA-ER has been tested on the standard economic emission power scheduling of hydrothermal energy test system consisting of four hydropower and three thermal plants. The problem has been investigated for the three case studies (i) ECS (Economic Cost Scheduling), (ii) ES (Economic Emission Scheduling) and (iii) ECES (Economic Cost & Emission Scheduling). The results obtained show that WCA-ER is superior to many other methods in the literature in bringing lower fuel cost and emissions.

关键词： Economic Emission Scheduling Multi-objective Optimization water cycle algorithm Evaporation Rate Fuel Emissions Exhaust Gas Emissions

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A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 1397-1412页

作者： Jeddi, Sima Sharifian, Saeed Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran

Nowadays, using cloud computing services is becoming very popular among corporations and dependent users because of its flexibility and diverse facilities. So, demand for its infrastructures is increasing exponentially and with this huge growth of demands, it is getting more challenging for cloud providers to serve all the user requests in a way that quality of service is high enough and service level agreement is also met. Along years, researchers found out that for an optimized resource allocation which provides required user quality of service it is needed to know the amount of future workload in advance so resources can be prepared. There are many methods introduced for cloud workload prediction but excessive changes in the number of requests in cloud workloads cause a reduction in their prediction accuracy. To solve this problem, we have proposed a novel hybrid wavelet neural network method which can efficiently model excessive changes in workload and predict the upcoming requests. For training the proposed method accurately, we have used two heuristic algorithms, Artificial Immune System and water cycle algorithm. The two mentioned heuristic algorithms are used for finding optimized parameters (such as bias and weight) for the wavelet neural network algorithm. The proposed wavelet neural network trained with heuristic algorithms is tested on real and standard cloud workloads. At last, the accuracy of the proposed method is thoroughly surveyed statistically and also compared with other state-of-the-art prediction methods. Simulation results show that our method improves MAPE error at least 10% in comparison to other rival prediction methods.

关键词： Cloud computing Workload prediction Wavelet transform Neural network water cycle algorithm Artificial intelligence system

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An efficient hybrid algorithm based on water cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

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SOFT COMPUTING 2019年第5期23卷 1699-1722页

作者： Khalilpourazari, Soheyl Khalilpourazary, Saman Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran Urmia Univ Technol Dept Mech Engn Fac Engn Orumiyeh Iran

This paper proposes a hybrid algorithm based on water cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. The spiral movement of moths in Moth-Flame Optimization algorithm is introduced into the water cycle algorithm to enhance its exploitation ability. In addition, to increase randomization in the new hybrid method, the streams in the water cycle algorithm are allowed to update their position using a random walk (Levy flight). The random walk significantly improves the exploration ability of the water cycle algorithm. The performance of the new hybrid water cycle-Moth-Flame Optimization algorithm (WCMFO) is investigated in 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of the WCMFO are compared to the other state-of-the-art metaheuristic algorithms. The results show that the hybrid method is able to outperform the other state-of-the-art metaheuristic algorithms in majority of the benchmark functions. To evaluate the efficiency of the WCMFO in solving complex constrained engineering and real-life problems, three well-known structural engineering problems are solved using WCMFO and the results are compared with the ones of the other metaheuristics in the literature. The results of the simulations revealed that the WCMFO is able to provide very competitive and promising results comparing to the other hybrid and metaheuristic algorithms.

关键词： Numerical optimization water cycle algorithm Moth-flame optimization Constrained engineering optimization Hybrid metaheuristic algorithm

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A modified water cycle evolutionary game theory algorithm to utilize QoS for IoT services in cloud-assisted fog computing environments

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JOURNAL OF SUPERCOMPUTING 2020年第7期76卷 5578-5608页

作者： Khansari, Mina Emami Sharifian, Saeed Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran

The Internet of Things (IoT) is rapidly gaining popularity as a result of the advancements in portable embedded devices and wireless protocols, enabling a new class of services. On the other hand, edge clouds provide IoT services as a new paradigm called fog computing. As the number of available IoT devices increases, more efficient methods are required to select the optimal combination of services out of several existing candidates in edge clouds while composing more complex IoT workflow tasks. So, cloud-assisted fog computing requires a platform for management, composition and provisioning of IoT services for IoT-cloud integration. Resent works have some weaknesses and did not consider some aspects of fog computing such as low latency, low energy and efficient resource allocation. We propose a cloud-based platform for management of IoT service selection and composition in fog computing to enhance QoS parameters such as bandwidth usage, latency and distributed resource utilization. In particular, we propose a multi-objective evolutionary game theory, enhanced by evaporation-based water cycle algorithm (EG-ERWCA) to optimize CPU usage, power consumption and latency of the IoT workflows in cloud-assisted fog computing environments. Many different real IoT workflows are used for evaluation of the proposed method in comparison with the state-of-art algorithms. Simulation results show that the overall quality of service is improved by 2.66 times compared to rival algorithms.

关键词： Fog computing Service selection Evolutionary game IoT water cycle algorithm

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A wavelet-based scheme for impact identification of framed structures using combined genetic and water cycle algorithms

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JOURNAL OF SOUND AND VIBRATION 2019年 443卷 25-46页

作者： Mahdavi, Seyed Hossein Rofooei, Fayaz R. Sadollah, Ali Xu, Chao Sharif Univ Technol Dept Civil Engn Tehran 111554313 Iran Northwestern Polytech Univ Sch Astronaut Xian 710072 Shaanxi Peoples R China Univ Sci & Culture Dept Mech Engn Tehran Iran

This paper presents a synthesis strategy for impact force localization and identification of framed structures in time-domain using a two-step wavelet-based fitness evaluation scheme in conjunction with genetic and water cycle algorithms. For this purpose, a straightforward approach is developed for sensitivity analysis of accelerations and spatial signal connecting the peak values. The proposed scheme is capable of using diverse scales of wavelet functions at considerably small sampling rates. A decimal genetic algorithm (GA) coding system and a recently developed water cycle algorithm (WCA) are improved to be used for impact localization and identification, respectively. The fitness evaluation is then modified for using the obtained results from sensitivity analysis rather than the acceleration itself. Numerical study is first conducted for a large space frame to evaluate the precision, convergence, and efficiency of the identified results using the proposed GA-WCA strategy to different measurement scenarios and initial estimates of the structural model. For comparison purposes, the robustness of WCA in identification step is thoroughly compared with other three state-of-the-art optimization algorithms i.e., particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and differential evolution (DE). Afterwards, an experimental validation study is carried out on a laboratory scale truss bridge. It is concluded that the computational performance of the proposed method is significantly better than the existing methods with respect to fitness evaluation. Results show that, even for the cases with a rough knowledge on structural parameters, the impact force is successfully identified with an excellent precision. This demonstrates the superiority of WCA strategy in handling the global search over large design space with large number of design variables. It is also concluded that the impact localization step is accomplished very fast, thus providing a nea

关键词： Impact identification Sensitivity analysis Wavelet Genetic algorithm water cycle algorithm

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Robust possibilistic programming for multi-item EOQ model with defective supply batches: Whale Optimization and water cycle algorithms

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NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 6587-6614页

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

This paper proposes a new mathematical model for multi-product economic order quantity model with imperfect supply batches. The supply batch is inspected upon arrival using "all or None" policy and if found defective, the whole batch will be rejected. In this paper, the goal is to determine optimal order quantity and backordering size for each product. To develop a realistic mathematical model of the problem, three robust possibilistic programming (RPP) approaches are developed to deal with uncertainty in main parameters of the model. Due to the complexity of the proposed RPP models, two novel meta-heuristic algorithms named water cycle and whale optimization algorithms are proposed to solve the RPP models. Various test problems are solved to evaluate the performance of the two novel meta-heuristic algorithms using different measures. Also, single-factor ANOVA and Tukey's HSD test are utilized to compare the effectiveness of the two meta-heuristic algorithms. Applicability and efficiency of the RPP models are compared to the Basic Possibilistic Chance Constrained Programming (BPCCP) model within different realizations. The simulation results revealed that the RPP models perform significantly better than the BPCCP model. At the end, sensitivity analyses are carried out to determine the effect of any change in the main parameters of the mathematical model on the objective function value to determine the most critical parameters.

关键词： Economic order quantity Imperfect batches Robust possibilistic programming water cycle algorithm Whale Optimization algorithm Chance constrained programming

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Jaya, harmony search and water cycle algorithms for solving large-scale real-life urban traffic light scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 37卷 58-72页

作者： Gao, Kaizhou Zhang, Yicheng Sadollah, Ali Lentzakis, Antonios Su, Rong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper studies a large-scale urban traffic light scheduling problem (LUTLSP). A centralized model is developed to describe the LUTLSP, where each outgoing flow rate is described as a nonlinear mixed logical switching function over the source link's density, the destination link's density and capacity, and the driver's potential psychological response to the past traffic light signals. The objective is to minimize the total network-wise delay time of all vehicles in a time window. Three metaheuristic optimization algorithms, named as Jaya algorithm, harmony search (HS) and water cycle algorithm (WCA) are implemented to solve the LUTLSP. Since we adopt a discrete-time formulation of LUTLSP, we firstly develop a discrete version of Jaya and WCA. Secondly, some improvement strategies are proposed to speed up the convergence of applied optimizers. Thirdly, a feature based search operator is utilized to improve the search performance of reported optimization methods. Finally, experiments are carried out based on the real traffic data in Singapore. The HS, WCA, Jaya, and their variants are evaluated by solving 11 cases of traffic networks. The comparisons and discussions verify that the considered metaheuristic optimization methods can effectively solve the LUTLSP considerably surpassing the existing traffic light control strategy.

关键词： Urban traffic light scheduling Large-scale traffic network Jaya water cycle algorithm Harmony search

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