Results 1  10
of
25
Parameter Estimation of Photovoltaic Models via Cuckoo Search
 Journal of Applied Mathematics, June 2013
"... Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo sp ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CSbased parameter estimation method is proposed to extract the parameters of singlediode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low RootMeanSquaredError (RMSE) value. The proposed method outperforms other algorithms applied in this study.
Swarm intelligence based algorithms: A critical analysis
 Evol. Intell
, 2014
"... ar ..."
(Show Context)
Improved Bat Algorithm Applied to Multilevel Image Thresholding,
 Article ID 176718, 16
, 2014
"... Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligen ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other stateoftheart algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other stateoftheart algorithms, improving quality of results in all cases and significantly improving convergence speed.
Firefly Penaltybased Algorithm for Bound Constrained MixedInteger Nonlinear Programming
"... In this article, we aim to extend the firefly algorithm (FA) to solve bound constrained mixedinteger nonlinear programming (MINLP) problems. An exact penalty continuous formulation of the MINLP problem is used. The continuous penalty problem comes out by relaxing the integrality constraints and by ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
In this article, we aim to extend the firefly algorithm (FA) to solve bound constrained mixedinteger nonlinear programming (MINLP) problems. An exact penalty continuous formulation of the MINLP problem is used. The continuous penalty problem comes out by relaxing the integrality constraints and by adding a penalty term to the objective function that aims to penalize integrality constraint violation. Two penalty terms are proposed, one is based on the hyperbolic tangent function and the other on the inverse hyperbolic sine function. We prove that both penalties can be used to define the continuous penalty problem, in the sense that it is equivalent to the MINLP problem. The solutions of the penalty problem are obtained using a variant of the metaheuristic FA for global optimization. Numerical experiments are given on a set of benchmark problems aiming to analyze the quality of the obtained solutions and the convergence speed. We show that the firefly penaltybased algorithm compares favorably with the penalty algorithm when the deterministic DIRECT or the simulated annealing solvers are invoked, in terms of convergence speed.
Synchronous firefly algorithm for cluster head selection inWSN,”The
 ScientificWorld Journal,
, 2015
"... Wireless Sensor Network (WSN) consists of small lowcost, lowpower multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Clusterbased approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH c ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Wireless Sensor Network (WSN) consists of small lowcost, lowpower multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Clusterbased approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH) offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NPHard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energyefficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.
Towards the novel reasoning among particles in pso by the use of rdf and sparql,”The Scientific World
"... The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further.
HeuristicBased Firefly Algorithm for Bound Constrained Nonlinear Binary Optimization
, 2014
"... Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper, we address the practical testing of a heuristicbased FA (HBFA) for computing optima of discrete nonlinear optimization problems, where the discrete variables are of binary type. An important issue in FA is the formula ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper, we address the practical testing of a heuristicbased FA (HBFA) for computing optima of discrete nonlinear optimization problems, where the discrete variables are of binary type. An important issue in FA is the formulation of attractiveness of each firefly which in turn affects its movement in the search space. Dynamic updating schemes are proposed for two parameters, one from the attractiveness term and the other from the randomization term. Three simple heuristics capable of transforming real continuous variables into binary ones are analyzed. A new sigmoid ‘erf ’ function is proposed. In the context of FA, three different implementations to incorporate the heuristics for binary variables into the algorithm are proposed. Based on a set of benchmark problems, a comparison is carried out with other binary dealing metaheuristics. The results demonstrate that the proposed HBFA is efficient and outperforms binary versions of differential evolution (DE) and particle swarm optimization (PSO). The HBFA also compares very favorably with angle modulated version of DE and PSO. It is shown that the variant of HBFA based on the sigmoid ‘erf ’ function with ‘movements in continuous space ’ is the best, both in terms of computational requirements and accuracy. 1
Article
"... Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semiactive suspension system using magnetorheological damper Mat Hussin Ab Talib and Intan Zaurah Mat Darus This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorith ..."
Abstract
 Add to MetaCart
(Show Context)
Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semiactive suspension system using magnetorheological damper Mat Hussin Ab Talib and Intan Zaurah Mat Darus This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semiactive (SA) suspension system using a magnetorheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force– displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSOtuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FAtuned IFL has been proven better than IFL–PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.
Application of genetic programming in shape optimization of concrete gravity dams by metaheuristics
, 2014
"... Abstract: A gravity dam maintains its stability against the external loads by its massive size. Hence, minimization of the weight of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with a computationally efficient a ..."
Abstract
 Add to MetaCart
Abstract: A gravity dam maintains its stability against the external loads by its massive size. Hence, minimization of the weight of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with a computationally efficient approach is introduced. Genetic programming (GP) in conjunction with metaheuristics is used for this purpose. As a case study, shape optimization of the Bluestone dam is presented. Pseudodynamic analysis is carried out on a total number of 322 models in order to establish a database of the results. This database is then used to find appropriate relations based on GP for design criteria of the dam. This procedure eliminates the necessity of the timeconsuming process of structural analyses in evolutionary optimization methods. The method is hybridized with three different metaheuristics, including particle swarm optimization, firefly algorithm (FA), and teachinglearningbased optimization, and a comparison is made. The results show that although all algorithms are very suitable, FA is slightly superior to other two algorithms in finding a lighter structure in less number of iterations. The proposed method reduces the weight of dam up to 14.6% with very low computational effort. PUBLIC INTEREST STATEMENT Dams are among the most important hydraulic structures which are used for various purposes. Gravity dams are solid concrete structures that maintain their stability against design loads from geometric shape to mass and strength of the concrete. Hence, weight minimization of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with minimum weight is introduced. The procedure is computationally efficient and considerably reduces the number of structural analyses required for the design. Genetic programming (GP) along with populationbased optimization approaches is used for this purpose. Optimization of the Bluestone dam is presented as a case study. By pseudodynamic analyses, a database is developed to find appropriate relations for design criteria of dam based on genetic programming. The developed equations are then hybridized with three different populationbased optimization techniques and a comparison is made.