scholarly journals Optimal Design of Isolated Mini-Grids with Deterministic Methods: Matching Predictive Operating Strategies with Low Computational Requirements

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4214 ◽  
Author(s):  
Andrea Micangeli ◽  
Davide Fioriti ◽  
Paolo Cherubini ◽  
Pablo Duenas-Martinez
Keyword(s):  
Optimal Design ◽  
Rural Areas ◽  
Search Space ◽  
Mixed Integer ◽  
Computational Time ◽  
Swarm Optimization ◽  
Load Following ◽  
Operating Strategies ◽  
Electricity Access ◽  
Better Than

The lack of electricity access is increasingly concentrated in rural areas of developing countries, in which mini-grids are often a suitable solution; however, given the high risks, it is crucial to minimize costs. This paper aims at analyzing existing methodologies for the optimal design of mini-grids combined with different operating strategies. Typical system operations, like the load-following (LFS) and cycle charging (CCS) strategies, are compared with the more demanding predictive strategies based on Mixed-Integer Linear Programming (MILP). The problem is formulated and solved with Particle Swarm Optimization (PSO), so to simulate traditional and predictive operating strategies. Two reformulations based on the proposed Search Space Update are also detailed and compared with the so-called one-shot MILP model, which is able to con-jointly optimize both the design and the operation of the system, in order to reduce computational requirements with the predictive strategy. The results, tailored with data from a rural mini-grid in Kenya, highlight that heuristic methodologies can perform better than the traditional MILP approach, both in terms of optimality and computational time, especially when advanced operating strategies are considered. Conventional operating strategies (LFS or CCS) appear to be sub-optimal, but require very little computational requirements, which makes them suitable for preliminary designs.

scholarly journals Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

2017 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Vina Ayumi ◽  
L.M. Rasdi Rere ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Contrast Enhancement ◽  
Harmony Search ◽  
Search Space ◽  
Optimization Method ◽  
Large Solution ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Simulation Results ◽  
Better Than

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

scholarly journals Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Oussama Ait Sahed ◽  
Kamel Kara ◽  
Mohamed Laid Hadjili
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Tracking Error ◽  
Particle Swarm ◽  
Search Space ◽  
Phase Behaviour ◽  
Computational Time ◽  
Distribution Law ◽  
Swarm Optimization ◽  
Practical Applications

A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

Heuristic Particle Swarm Optimization for Scheduling Chemical Flowshop with Limited Intermediate Product Storage

2014 ◽  
Vol 1037 ◽  
pp. 514-517
Author(s):  
Qi Tang ◽  
Xiao Ye Zhou ◽  
Jian Xun Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Intermediate Product ◽  
Particle Swarm ◽  
Batch Scheduling ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Complicated Problem ◽  
Multi Stage ◽  
Better Than ◽  
Product Storage

This paper considers integration of batching and scheduling with features on batch scheduling such as multi-product facilities, multi-stage. For this complicated problem, we present a mixed integer model (EM) and Heuristic Particle Swarm Optimization (HPSO). We proposed Strategies to improve the HPSO. Computations show that both methods are efficient. In addition, HPSO are better than EM.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

scholarly journals An Improved Crow Search Algorithm for Data Clustering

2020 ◽  
Vol 8 (1) ◽  
pp. 86-101 ◽  
Author(s):  
Vivi Nur Wijayaningrum ◽  
Novi Nur Putriwijaya
Keyword(s):  
Data Clustering ◽  
Search Algorithm ◽  
Search Space ◽  
Large Datasets ◽  
The Other ◽  
Computational Time ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
High Dimensions ◽  
Swarm Optimization

Metaheuristic algorithms are often trapped in local optimum solutions when searching for solutions. This problem often occurs in optimization cases involving high dimensions such as data clustering. Imbalance of the exploration and exploitation process is the cause of this condition because search agents are not able to reach the best solution in the search space. In this study, the problem is overcome by modifying the solution update mechanism so that a search agent not only follows another randomly chosen search agent, but also has the opportunity to follow the best search agent. In addition, the balance of exploration and exploitation is also enhanced by the mechanism of updating the awareness probability of each search agent in accordance with their respective abilities in searching for solutions. The improve mechanism makes the proposed algorithm obtain pretty good solutions with smaller computational time compared to Genetic Algorithm and Particle Swarm Optimization. In large datasets, it is proven that the proposed algorithm is able to provide the best solution among the other algorithms.

scholarly journals A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7881
Author(s):  
Tatiana González Grandón ◽  
Fernando de Cuadra García ◽  
Ignacio Pérez-Arriaga
Keyword(s):  
Programming Model ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Spot Prices ◽  
Load Following ◽  
Comparison Results ◽  
Solar Battery ◽  
Operating Strategies ◽  
Uncertain Input ◽  
Hybrid Solar Battery

Renewable-powered “undergrid mini-grids” (UMGs) are instrumental for electrification in developing countries. An UMG can be installed under a—possibly unreliable— main grid to improve the local reliability or the main grid may “arrive” and connect to a previously isolated mini-grid. Minimising costs is key to reducing risks associated with UMG development. This article presents a novel market-logic strategy for the optimal operation of UMGs that can incorporate multiple types of controllable loads, customer smart curtailment based on reliability requirements, storage management, and exports to and imports from a main grid, which is subject to failure. The formulation results in a mixed-integer linear programming model (MILP) and assumes accurate predictions of the following uncertain parameters: grid spot prices, outages of the main grid, solar availability and demand profiles. An AC hybrid solar-battery-diesel UMG configuration from Nigeria is used as a case example, and numerical simulations are presented. The load-following (LF) and cycle-charging (CC) strategies are compared with our predictive strategy and HOMER Pro’s Predictive dispatch. Results prove the generality and adequacy of the market-logic dispatch model and help assess the relevance of outages of the main grid and of spot prices above the other uncertain input factors. Comparison results show that the proposed market-logic operation approach performs better in terms of cost minimisation, higher renewable fraction and lower diesel use with respect to the conventional LF and CC operating strategies.

scholarly journals Integer Programming for Learning Directed Acyclic Graphs from Continuous Data

2020 ◽  
pp. ijoo.2019.0040
Author(s):  
Hasan Manzour ◽  
Simge Küçükyavuz ◽  
Hao-Hsiang Wu ◽  
Ali Shojaie
Keyword(s):  
Programming Model ◽  
Score Function ◽  
Directed Acyclic Graphs ◽  
Mixed Integer ◽  
Computational Time ◽  
Quadratic Program ◽  
Acyclic Graphs ◽  
Continuous Relaxation ◽  
Integer Quadratic Program ◽  
Better Than

Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this paper, we study the problem of learning an optimal DAG from continuous observational data. We cast this problem in the form of a mathematical programming model that can naturally incorporate a superstructure to reduce the set of possible candidate DAGs. We use a negative log-likelihood score function with both l0 and l1 penalties and propose a new mixed-integer quadratic program, referred to as a layered network (LN) formulation. The LN formulation is a compact model that enjoys as tight an optimal continuous relaxation value as the stronger but larger formulations under a mild condition. Computational results indicate that the proposed formulation outperforms existing mathematical formulations and scales better than available algorithms that can solve the same problem with only l1 regularization. In particular, the LN formulation clearly outperforms existing methods in terms of computational time needed to find an optimal DAG in the presence of a sparse superstructure.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

scholarly journals A Multi-Objective Approach toward Optimal Design of Sustainable Integrated Biodiesel/Diesel Supply Chain Based on First- and Second-Generation Feedstock with Solid Waste Use

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2261
Author(s):  
Evgeniy Ganev ◽  
Boyan Ivanov ◽  
Natasha Vaklieva-Bancheva ◽  
Elisaveta Kirilova ◽  
Yunzile Dzhelil
Keyword(s):  
Supply Chain ◽  
Optimal Design ◽  
Solid Waste ◽  
Second Generation ◽  
Agricultural Land ◽  
Optimization Problems ◽  
Biodiesel Production ◽  
Mixed Integer ◽  
Economic Criterion ◽  
Multi Objective

This study proposes a multi-objective approach for the optimal design of a sustainable Integrated Biodiesel/Diesel Supply Chain (IBDSC) based on first- (sunflower and rapeseed) and second-generation (waste cooking oil and animal fat) feedstocks with solid waste use. It includes mixed-integer linear programming (MILP) models of the economic, environmental and social impact of IBDSC, and respective criteria defined in terms of costs. The purpose is to obtain the optimal number, sizes and locations of bio-refineries and solid waste plants; the areas and amounts of feedstocks needed for biodiesel production; and the transportation mode. The approach is applied on a real case study in which the territory of Bulgaria with its 27 districts is considered. Optimization problems are formulated for a 5-year period using either environmental or economic criteria and the remainder are defined as constraints. The obtained results show that in the case of the economic criterion, 14% of the agricultural land should be used for sunflower and 2% for rapeseed cultivation, while for the environmental case, 12% should be used for rapeseed and 3% for sunflower. In this case, the price of biodiesel is 14% higher, and the generated pollutants are 6.6% lower. The optimal transport for both cases is rail.

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