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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. …”
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Fault Detection Relevant, Neural Network and Evolutionary Algorithm based Model for a Single-shaft Industrial Gas Turbine
Published 2009“…In this paper the result of an attempt to develop a substitute nonlinear model based on multilayer neural network (MLNN) and evolutionary algorithm (EA) for a single-shaft gas turbine having IGVs and VSVs is presented. …”
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Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris
Published 2019“…The proposed co-simulation process is developed by coupling building energy simulation (BES) software, Energy Plus with multi-objective evolutionary programming (MOEP) algorithm which is implemented in Matlab using coupling software, BCVTB. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang
Published 2018“…The Seed Disperser Ant Algorithm (SDAA) is developed based on the evolution or expansion process of Seed Disperser Ant (Aphaenogaster senilis) colony. …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail
Published 2024“…In this study, a new optimisation algorithm termed Hybrid Evolutionary-Barnacles Mating Optimisation (HEBMO) was initially formulated to solve optimisation problems. …”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks
Published 2015“…A hybrid approach that combines ANN and an evolutionary optimization technique, genetic algorithm (GA) is used for the development of a short term load forecast (STLF) model. …”
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Short-Term Electricity Price Forecasting via Hybrid Backtracking Search Algorithm and ANFIS Approach
Published 2019“…A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most influential subsets of input variables with maximum relevancy and minimum redundancy. …”
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CTJ: Input-output based relation combinatorial testing strategy using Jaya algorithm
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Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm
Published 2013“…Finally, non-dominated sorting genetic algorithm-II as evolutionary optimization approach was used for multi-objective optimization of the micro-end milling process. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
Published 2018“…The present study developed artificial intelligence model, called Shark Machine Learning Algorithm (SMLA) to provide optimal operational rules. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness
Published 2024“…A comprehensive assessment utilizing standard mathematical functions such as Fonseca-Fleming (FON), Kursawe (KUR), ZDT1, ZDT2, ZDT3, and ZDT6, and a comparison with state-of-the-art benchmarks in the field such as the Multi-Objective Evolutionary Algorithm (MOEA), Non-Dominated Sorting Genetic Algorithm (NSGA-II), and NSGA-III, validate the efficiency of MEPSOLA. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
Published 2011“…Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). …”
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