PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives

Ghadimi, Noradin; Afkousi-Paqaleh, Mohammad; Nouri, Alireza

doi:10.1109/jsyst.2013.2252865

Cited by 61 publications

(24 citation statements)

References 22 publications

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“…Particle Swarm Optimization (PSO) 18,[53][54][55][56] are some of the examples of population-based nature inspired metaheuristic algorithms. 57 The query optimization mechanisms regarding the non-deterministic algorithms and their anatomy are discussed in Section 3.1.2.…”

Section: Overview Of Non-deterministic Mechanismmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

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Section: Overview Of Non-deterministic Mechanismmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

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“…51-53 NSPSO as one of the most powerful MOEAs improves the abilities of the basic form of PSO by better use of particle best (pbest). 56,57 The problem of the simple PSO is that dominance comparisons are not applied in the updating process of particles. 55 On the other hand, global best (gbest) shows the best-found position of an entire population and any particle.…”

Section: Task Scheduling Using Nspsomentioning

confidence: 99%

“…55 On the other hand, global best (gbest) shows the best-found position of an entire population and any particle. 56,57 The problem of the simple PSO is that dominance comparisons are not applied in the updating process of particles. For solving this issue and enhancing the sharing level between particles in the swarm, NSPSO combines the whole population of N pbest and N of these particles' offspring to form a temporary population of 2 N particles.…”

Section: Task Scheduling Using Nspsomentioning

confidence: 99%

A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

Wakil

Badfar

Dehghani

et al. 2019

Concurrency and Computation

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Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution.Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms. KEYWORDScloud computing, multi-objective optimization, particle swarm optimization algorithm, task scheduling INTRODUCTIONCloud computing as an innovative distributed computing can provide dynamic resources, which are highly virtualized. 1,2 In addition, cloud providers deliver their services (such as software, hardware, platform, and storage) over the Internet. 3,4 Therefore, there are no shrink-wrapped boxes containing discs or hardware for users to buy. The cloud providers typically charge monthly recurring fees based on usage. 5-7 Many benefits, including the cost reduction and flexibility, make cloud computing to be superior technology in computing. 8 Due to the numerous benefits of cloud computing, many users have been attracted to use cloud-based services. 9,10 There are some issues in the cloud computing that must be addressed, such as resource discovery, 11,12 trust evaluation, 13-15 task, 16-18 resource allocation, 19-21 and so on. 22 Among them, task scheduling has a primary role in reaching the efficiency of distributed computing systems such as clouds and grids. [23][24][25] In the cloud systems, a task scheduler allocates the submitted tasks to proper resources for performing. 26-28 From the creation of cloud computing, task scheduling has gained significant attention, and many researchers have investigated many methods based on artificial intelligence-based algorithms to solve it. 29 It is responsible for finding potential cloud resources for satisfying the users' requests in the cloud environment. The main aim of task scheduler is decreasing the total cost, completion time and the computational complexity. 30,31 However, most of the existing research has focused on a single objective task scheduling in cloud systems. However, in real cases, more than one factor should be considered. 29 Since task scheduling is an NP-hard issue, many meta-heuristic algo...

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“…Year kernel-based forecasting [6] 2012 PSO based fuzzy model for distribution systems forecasting [7] 2013 Hybrid methodology for short-term load forecasting [8] 2014 A Hybrid algorithm for power prediction [9] 2017 GA-based non-linear AR prediction for load and wind speed [10] 2018 PSO-SVM based forecasting [11] 2019 ANN has various advantages over conventional methods i.e. capability of fault-tolerance, non-linear modeling, etc.…”

Section: Table-1 Various Hybrid Forecasting Approaches Forecasting Momentioning

confidence: 99%

Electric Forecasting using Nature Inspired Optimization Techniques

Singh*,

Srivastava,

Singh

2019

IJEAT

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Now a day, there exists huge competition among power industries in terms of fulfilling various customers' electrical needs. Reliable and quality power supply is no doubt a basic need for all power consumers. Moreover, planning & operation engineers also targets for proper unit commitment, economic power dispatch, etc., and highly depend upon good power system planning. Therefore, Electric Forecasting (EF) is a major criterion for power engineers. In this manuscript, Artificial Neural Network (ANN), being a well established tool for modeling non-linear and black box systems, is used to forecast hydro generation power plant, energy met and peak demand of India.Furthermore, in this competitive world, ANN model is further optimized using genetic algorithm (GA) and particle swarm optimization (PSO) to explore accurate forecasting model with minimal amount of error. These optimization methods explore highly diversified search area, resulting in more accurate forecasting results in comparison to ANN when trained with standard back propagation training algorithm. India. His research area includes power system planning, transformer, energy efficiency, load forecasting, neural network. Navneet Kumar Singh received the PhD degree from Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India. Currently he is with Electrical Engineering Department, MNNIT Allahabad, India. His research area includes load forecasting, power system planning and Application of Artificial Intelligence (AI) techniques in power system.

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PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives

Cited by 61 publications

References 22 publications

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Deterministic and non‐deterministic query optimization techniques in the cloud computing

A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

Electric Forecasting using Nature Inspired Optimization Techniques

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