scholarly journals Swarm intelligence for groundwater management optimization

2010 ◽  
Vol 13 (3) ◽  
pp. 520-532 ◽  
Author(s):  
A. Sedki ◽  
D. Ouazar
Keyword(s):  
Swarm Intelligence ◽  
Groundwater Management ◽  
Simulation Optimization ◽  
Groundwater Resources ◽  
Flow Simulation ◽  
Optimization Techniques ◽  
Optimization Models ◽  
Simulation Code ◽  
Management Problems ◽  
Management Optimization

This paper presents some simulation–optimization models for groundwater resources management. These models couple two of the most successful global optimization techniques inspired by swarm intelligence, namely particle swarm optimization (PSO) and ant colony optimization (ACO), with one of the most commonly used groundwater flow simulation code, MODFLOW. The coupled simulation–optimization models are formulated and applied to three different groundwater management problems: (i) maximization of total pumping problem, (ii) minimization of total pumping to contain contaminated water within a capture zone and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods. The results of PSO- and ACO-based models are compared with those produced by other methods previously presented in the literature for the three case studies considered. It is found that PSO and ACO are promising methods for solving groundwater management problems, as is their ability to find optimal or near-optimal solutions.

Improved Flower Pollination Algorithm for Optimal Groundwater Management

2020 ◽  
Vol 19 (03) ◽  
pp. 2050022
Author(s):  
Sedki Akram
Keyword(s):  
Groundwater Management ◽  
Large Scale ◽  
Flow Simulation ◽  
Simulation Models ◽  
Flower Pollination Algorithm ◽  
Solution Quality ◽  
Aquifer System ◽  
Practical Applications ◽  
Flower Pollination ◽  
Management Problems

Groundwater management problems are typically of a large-scale nature, involving complex nonlinear objective functions and constraints, which are commonly evaluated through the use of numerical simulation models. Given these complexities, metaheuristic optimization algorithms have recently become popular choice for solving such complex problems which are difficult to solve by traditional methods. However, the practical applications of metaheuristics are severely challenged by the requirement of large number of function evaluations to achieve convergence. To overcome this shortcoming, many new metaheuristics and different variants of existing ones have been proposed in recent years. In this study, a recently developed algorithm called flower pollination algorithm (FPA) is investigated for optimal groundwater management. The FPA is improved, combined with the widely used groundwater flow simulation model MODFLOW, and applied to solve two groundwater management problems. The proposed algorithm, denoted as IFPA, is first tested on a hypothetical aquifer system, to minimize the total pumping to contain contaminated groundwater within a capture zone. IFPA is then applied to maximize the total annual pumping from existing wells in Rhis-Nekor unconfined coastal aquifer on the northern of Morocco. The obtained results indicate that IFPA is a promising method for solving groundwater management problems as it outperforms the standard FPA and other algorithms applied to the case studies considered, both in terms of convergence rate and solution quality.

scholarly journals Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

2021 ◽  
Vol 13 (24) ◽  
pp. 13551
Author(s):  
Mohamed Hussein ◽  
Abdelrahman E. E. Eltoukhy ◽  
Amos Darko ◽  
Amr Eltawil
Keyword(s):  
Swarm Intelligence ◽  
Construction Projects ◽  
Variance Reduction ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Computation Time ◽  
Bat Algorithm ◽  
Optimization Models ◽  
Planning Decisions ◽  
Whale Optimization

Off-site construction is a modern construction method that brings many sustainability merits to the built environment. However, the sub-optimal planning decisions (e.g., resource allocation, logistics and overtime planning decisions) of off-site construction projects can easily wipe away their sustainability merits. Therefore, simulation modelling—an efficient tool to consider the complexity and uncertainty of these projects—is integrated with metaheuristics, developing a simulation-optimization model to find the best possible planning decisions. Recent swarm intelligence metaheuristics have been used to solve various complex optimization problems. However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence metaheuristics to solve the stochastic time–cost trade-off problem with the aid of parallel computing and a variance reduction technique to reduce the computation time. These five metaheuristics include the firefly algorithm, grey wolf optimization, the whale optimization algorithm, the salp swarm algorithm, and one improved version of the well-known bat algorithm. The literature analysis of the simulation-optimization models in the construction field shows that: (1) discrete-event simulation is the most-used simulation method in these models, (2) most studies applied genetic algorithms, and (3) very few studies used computation time reduction techniques, although the simulation-optimization models are computationally expensive. The five selected swarm intelligence metaheuristics were applied to a case study of a bridge deck construction project using the off-site construction method. The results further show that grey wolf optimization and the improved bat algorithm are superior to the firefly, whale optimization, and salp swarm algorithms in terms of the obtained solutions’ quality and convergence behaviour. Finally, the use of parallel computing and a variance reduction technique reduces the average computation time of the simulation-optimization models by about 87.0%. This study is a step towards the optimum planning of off-site construction projects in order to maintain their sustainability advantages.

scholarly journals Notes on Using Simulation-Optimization Techniques in Traffic Simulation

2017 ◽  
Vol 27 ◽  
pp. 881-888 ◽  
Author(s):  
Xavier Ros-Roca ◽  
Lídia Montero ◽  
Jaume Barceló
Keyword(s):  
Traffic Simulation ◽  
Simulation Optimization ◽  
Optimization Techniques

scholarly journals Optimizing Reduction of Groundwater Discharge to Control Land Subsidence (Case study: Nadjafabad Aquifer)

2021 ◽  
Author(s):  
hamid Kardan moghaddam ◽  
Zahra Rahimzadeh kivi ◽  
Fatemeh Javadi ◽  
Mohammad Heydari
Keyword(s):  
Land Subsidence ◽  
Groundwater Resources ◽  
Treatment Strategies ◽  
Flow Simulation ◽  
Maximum Amount ◽  
Water Withdrawal ◽  
Ground Subsidence ◽  
Risk Zoning ◽  
Dry Conditions ◽  
The Impact

Abstract This study evaluates and predicts the ground subsidence that happens due to the haphazard operation of groundwater resources. Also, several strategies have been developed to control this unpleasant phenomenon. For this purpose, groundwater flow simulation has been conducted using MODFLOW numerical model, and subsidence simulation in Najafabad plain has been done using SUB package under three climatic scenarios for future periods. Examination of the simulation results shows that the amount of land subsidence will increase with the aquifer operation's continuation. The maximum amount of subsidence for 6 years in drought conditions will be 23 cm at the aquifer's outlet. According to the land subsidence results at the aquifer, risk zoning of the aquifer operation was done to develop a solution to reduce the withdrawal of groundwater resources to control subsidence. Therefore, risk zoning was performed using land use and the extent of operation of groundwater resources. The results showed that the north-eastern part of the aquifer has the maximum risk of subsidence. According to the obtained results from subsidence risk zoning, scenarios of reduced water withdrawal from the aquifer in its outlet were developed. The treatment strategies results showed that the maximum amount of subsidence in wet, normal and dry conditions will be 10, 14 and 18 cm, respectively. These results indicate a 14% improvement in the quantitative condition of the aquifer in wet conditions, 10% in normal conditions and 7% in dry conditions in the total aquifer of Najafabad. Improvement of conditions by simulation shows the impact of the importance of optimal utilization of groundwater resources.

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

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