A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

García-Ródenas, Ricardo; Jiménez-Linares, Luis; López-Gómez, Julio Alberto

doi:10.1016/j.asoc.2019.03.011

Cited by 46 publications

(21 citation statements)

References 62 publications

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“…The goal of the optimisation algorithm is to determine the best parameter values of the system under different conditions (Ahmed et al, 2016). Recently, the gravitational search algorithm (GSA) proposed by Rashedi et al (2009) has been applied to tackle various optimisation issues such as unconstrained global optimisation problems (García-Ródenas et al, 2019), hydrology (Karami et al, 2019) and in the geothermal power plant optimisation (Özkaraca and Keçebaş, 2019). Particle Swarm Optimisation (PSO) algorithm has been used in different fields such as sediment yield forecasting (Meshram et al, 2019), operation rule derivation of hydropower reservoir (Feng et al, 2019) and semi-supervised data clustering (Lai et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Zubaidi

Ortega‐Martorell

Kot

et al. 2020

Water Resour Manage

112

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Section: Introductionmentioning

confidence: 99%

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Zubaidi

Ortega‐Martorell

Kot

et al. 2020

Water Resour Manage

112

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“…The problem (5) may contain many local minima and optimizers such as metaheuristics may be good choices to address it. These methods have been developed intensively for unconstrained optimization (García- Ródenas et al 2019) leading to the consideration of another way to scalarize a bi-objective optimization problem. Assuming that the exploitation measure satisfies w X (y) > 0 for all y ∈ D , the following sampling problem is stated:…”

Section: Bi-objective Infill Sampling Criterionmentioning

confidence: 99%

A surrogate-based cooperative optimization framework for computationally expensive black-box problems

2020

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Most parallel surrogate-based optimization algorithms focus only on the mechanisms for generating multiple updating points in each cycle, and rather less attention has been paid to producing them through the cooperation of several algorithms. For this purpose, a surrogate-based cooperative optimization framework is here proposed. Firstly, a class of parallel surrogate-based optimization algorithms is developed, based on the idea of viewing the infill sampling criterion as a bi-objective optimization problem. Each algorithm of this class is called a Sequential Multipoint Infill Sampling Algorithm (SMISA) and is the combination resulting from choosing a surrogate model, an exploitation measure, an exploration measure and a multiobjective optimization approach to its solution. SMISAs are the basic algorithms on which collaboration mechanisms are established. Many SMISAs can be defined, and the focus has been on scalar approaches for bi-objective problems such as the -constrained method, revisiting the Parallel Constrained Optimization using Response Surfaces (CORS-RBF) method and the Efficient Global Optimization with Pseudo Expected Improvement (EGO-PEI) algorithm as instances of SMISAs. In addition, a parallel version of the Lower Confidence Bound-based (LCB) algorithm is given as a member within the SMISA class. Secondly, we propose a cooperative optimization framework between the SMISAs. The cooperation between SMISAs occurs in two ways: (1) they share solutions and their objective function values to update their surrogate models and (2) they use the sampled points obtained from different SMI-SAs to guide their own search process. Some convergence results for this cooperative framework are given under weak conditions. A numerical comparison between EGO-PEI, Parallel CORS-RBF and a cooperative method using both, named CPEI, shows that CPEI improves the performance of the baseline algorithms. The numerical results were derived from 17 analytic tests and they show the reduction of wallclock time with respect to the increase in the number of processors.

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“…This algorithm is called the Simulated Annealing (SA) method, which has been utilized to determine the parameters of the SD and DD model of SC and PV modules. Moreover, it has been demonstrated that the meta-heuristic (MH) optimization techniques allow building an effective PV modulator according to various criteria such as precision, consistency, convergence speed, calculation efficiency and the reduced number of control parameters [23,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. These algorithms can be classified into four categories, evolutionary algorithms (Genetic Algorithm (GA), differential Evolution (DE), and Confidence-Weighted (SCW), physics-based algorithms (Wind Driven Optimization (WDO), Flower Pollination Algorithm (FPA), and Gravitational Search Algorithm (GSA)), swarm-based algorithms (Artificial bee colony (ABC), particle swarm optimization (PSO), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), improved whale optimization algorithm (IWOA), a performance-guided JAYA (PGJAYA), Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer (C-HCLPSO), Improved Lozi Map based Chaotic Optimization Algorithm (ILCOA), biogeography-based heterogeneous cuckoo search (BHCS) algorithm, and symbiotic organisms search (SOS) algorithm), and human-based algorithms (HBA).…”

Section: Introductionmentioning

confidence: 99%

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

et al. 2020

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Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547x10-4 , 7.64801x10-4 , and 7.59756 x10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

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A Memetic Chaotic Gravitational Search Algorithm for unconstrained global optimization problems

Cited by 46 publications

References 62 publications

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

A surrogate-based cooperative optimization framework for computationally expensive black-box problems

Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules

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