Application of Intelligent Water Drops Algorithm in Reservoir Operation

Dariane, Alireza B.; Sarani, S.

doi:10.1007/s11269-013-0441-x

Cited by 25 publications

(12 citation statements)

References 18 publications

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“…where g is Newton's gravitational constant, M is the mass, and c is the speed of light. Black hole algorithm belongs to the swarm intelligence algorithm, which are inspired either by living bodies, like ants [88], bees [89], fishes [90], bats [91], krill herds [92], fireflies [93], fruit flies [94], bacteria's [95], or by other natural phenomena, like gravitation [96], big-bang [97], or intelligent water drop [98]. Black hole optimization is used in a wide range of NP-hard optimization problems, like investigating the critical slip surface of soil slope [99], solving the non-unicost set covering problem [100], optimization of consignment-store-based supply chain [101], thermodynamic optimization of a Penrose process [102], power flow optimization [103], and design of electromagnetic devices [104], but one of its most important application fields is the clustering.…”

Section: Black Hole Optimization-based Clusteringmentioning

confidence: 99%

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

et al. 2019

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In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.

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Section: Black Hole Optimization-based Clusteringmentioning

confidence: 99%

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

et al. 2019

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“…The idea of the IWD algorithm was first proposed by Shah-Hosseini [ 32 ] in 2009. Since then, the algorithm has been applied to solve several optimization problems, such as the n-queen puzzle multidimensional knapsack problem [ 33 ], multilevel thresholding of gray-level images [ 33 ], multi-objective job shop scheduling in scheduling system [ 40 ], optimum reservoir operation in water resources systems [ 41 ], robot path planning in robotics [ 42 ], economic load dispatch problem in power systems [ 43 ], feature selection with rough set [ 44 ], search and selection optimization processes [ 45 ], and examination time-tabling scheduling problem [ 46 ]. (Reference could be made to [ 47 ] for a comprehensive summary of the various problems that have been successfully solved using the IWD algorithm).…”

Section: Metaheuristic Solution To Mdvrpmentioning

confidence: 99%

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

et al. 2018

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The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

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“…IWD is a widely known swarm intelligence technique and GA belongs to the class of evolutionary algorithms. Both have been successfully applied to solve many optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

Kalra

Singh

2019

Concurrency and Computation

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Cloud Computing is increasingly favored by scientists for executing large-scale workflow applications due to its appealing features such as availability of huge infrastructure, low operating costs, easy accessibility, and high scalability. Moreover, the resources in cloud are provisioned as per user's requirement following pay-per-use model. Scheduling workflows in cloud requires orchestration of tasks onto heterogeneous resources while complying with the task dependencies as well as user's Quality of Service (QoS) demands. The problem becomes challenging if there are multiple QoS requirements which conflict with each other. Recent studies attempted to minimize the makespan and execution cost under deadline and/or budget constraints. However, the failure of resources in cloud poses a serious threat to successful execution of these applications. Hence, it becomes essential to consider reliability of resources in addition to other parameters. This paper presents a workflow scheduling policy which minimizes makespan and execution cost while maximizing the reliability of executing workflows under user specified deadline and budget restraints. We have devised a hybrid of Intelligent Water Drops algorithm and Genetic Algorithm (IWD-GA) to accomplish the desired goal. It provides a set of non-dominated solutions with different makespan, cost, and reliability to offer users more flexibility in selecting a solution as per their preferences. The evaluation of the proposed algorithm has been carried based on two performance metrics, ie, hypervolume and two set coverage. The simulation results thus obtained using four diverse scientific workflows confirm that IWD-GA outperforms non-dominated sort genetic algorithm (NSGA-II) and hybrid particle swarm optimization (HPSO) with regard to accuracy and diversity.

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Application of Intelligent Water Drops Algorithm in Reservoir Operation

Cited by 25 publications

References 18 publications

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

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