Search Results - (( using evolutionary modified algorithm ) OR ( simulation optimization _ algorithm ))
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1
The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo...
Published 2025“…According to simulation findings, the technique can be used to optimize the PID controller parameters settings for the IAVC of the CSLRFM and a reduction in the mechanical vibrations. …”
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2
Application of Evolutionary Algorithm for Assisted History Matching
Published 2014“…While the automatic method focus on optimization which is normally computer based. In this project, we will define and discuss the application of evolutionary algorithm in assisted history matching. …”
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Final Year Project -
3
Optimisation of automatic generation control performance in two-area power system with pid controllers using mepso / Lu Li
Published 2018“…The AGC in two-area power system was constructed and simulated by using MATLAB R2017b software. From the simulation results, it was found that with the same number of PID controllers, the performance of AGC optimised by using MEPSO-TVAC algorithm is better in terms of overshoot and fitness value than using EPSO and PSO algorithms. …”
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Thesis -
4
Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm
Published 2018“…The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. …”
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5
PID controller adjustment for MA-LFC by using imperialist competitive algorithm
Published 2013“…In this paper a new evolutionary computing method based on Imperialist Competitive Algorithm (ICA) is used for tuning the elements of a PID controller which is applied in a Multi Area Load Frequency Control System (MA-LFC). …”
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Conference or Workshop Item -
6
Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy
Published 2024“…Advancing multi-objective optimization techniques for cancer treatment strategies, the study strategically incorporates Swarm Intelligence (SI) and Evolutionary Algorithms (EA). …”
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7
Optimal power flow with stochastic solar power using barnacles mating optimizer
Published 2021“…This work proposes the implementation of recent evolutionary metaheuristic algorithm namely, barnacles mating optimizer (BMO) to solve the Optimal Power Flow (OPF) issue. …”
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Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array
Published 2019“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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9
Development of a robust intelligent controller for a semi-active car suspension system
Published 2022“…Commonly, the Fuzzy rules are optimized using offline optimization methods such as Differential Evolutionary (DE), Particle Swarms Optimization (PSO), or Artificial Neural Network (ANN) algorithms. …”
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10
Enhancing benchmark optimization with evolutionary random approach: A comparative analysis of modified adaptive bats sonar algorithm (MABSA)
Published 2025“…This article presents a novel hybrid algorithm, combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA), and compares its performance with the original MABSA. …”
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Conference or Workshop Item -
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Hybrid evolutionary optimization algorithms: A case study in manufacturing industry
Published 2014“…Such complex problems of vagueness and uncertainty can be handled by the hybrid evolutionary intelligence algorithms. In this chapter, a new hybrid evolutionary optimization based methodology using a specific non-linear membership function, named as modified S-curve membership function, is proposed. …”
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12
Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
Published 2018“…In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…Evolutionary algorithms have been extensively used to resolve problems associated with multiple and often conflicting objectives. …”
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Thesis -
14
Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…Two multi-objective fed-batch models are also used as case studies to verify the performance of the proposed algorithm. …”
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15
Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
Published 2019“…Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. …”
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Evolutionary Multiobjective optimization for automatic generation Of Neural game controller
Published 2013“…This research presents the result of implementing evolutionary algorithms towards computational intelligence in Tower Defense game (TD game). …”
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A multi-objective evolutionary approach for fuzzy optimization in production planning
Published 2007“…An ad hoc Pareto-based multi-objective evolutionary algorithm is proposed to capture multiple non dominated solutions in a single run of the algorithm. …”
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Conference or Workshop Item -
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Optimum MV Feeder Routing and Substation siting and rating in Distribution Network
Published 2014“…This paper proposes an evolutionary algorithm to determine the optimum distribution substation placement and sizing by using the particle swarm optimization algorithm and optimum feeder routing using modified minimum spanning tree algorithm. …”
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20
Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm
Published 2017“…The application of artificial neural network (ANN) to generate the production model is used to improve the modelling accuracy. The model output optimisation by genetic algorithm (GA) produces higher biogas production compared to the optimisation using statistical methods. …”
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