A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms

Mobin, Mohammadsadegh; Mousavi, Seyed Mohsen; Komaki, Mohammad; Tavana, Madjid

doi:10.1016/j.measurement.2017.10.009

Cited by 39 publications

(15 citation statements)

References 41 publications

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“…As an approach to improve this response, an online tuning mechanism has been suggested. A number of tuning methods have been proposed in the literature [34][35][36]. In this paper, a different tuning algorithm is proposed to add an online tuning feature to the PI controller.…”

Section: Proposed Pi Controllers With Online Tuning Mechanismmentioning

confidence: 99%

Capacitor Voltage Balancing with Online Controller Performance-Based Tuning for a Switch-Sharing-Based Multilevel Inverter

et al. 2021

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Switch-sharing-based multilevel inverters offer great advantages in terms of efficiency improvement and output quality enhancement for low-power photovoltaic (PV) applications. However, the capacitor voltage balancing issue may critically deteriorate the output voltages, and thus could nullify the aforementioned benefits. Hence, this paper proposes a capacitor voltage balancing solution based on a buck-boost converter with performance-based tuning controllers to address the issue. The proposed controllers are designed based on the proportional-integral (PI) configuration equipped with an online tuning mechanism. The main purpose of the online tuning mechanism is to fine tune the proportional gain according to the DC input voltage, based on the measured output current total harmonic distortion (THD) performance of the inverter, while at the same time preventing the controller from reaching a state of saturation. By using the actual measurement of current THD performance, online tuning accuracy can be improved since no ideal condition is assumed, and thus, the associated error can be minimized. Simulation and experimental results reveal that the capacitor voltages can be balanced at high modulation indexes with improved current harmonic performance able to be be acquired at the inverter’s output.

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Section: Proposed Pi Controllers With Online Tuning Mechanismmentioning

confidence: 99%

Capacitor Voltage Balancing with Online Controller Performance-Based Tuning for a Switch-Sharing-Based Multilevel Inverter

et al. 2021

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“…The time consumption was less when tuned with CRS as compared to F-Race and Revac. Mohammadsadegh et al [24] has applied a hybrid desirability function approach on a multi-objective Particle Swarm Optimization (MOPSO) and a fast, nondominated sorting Genetic Algorithm (NSGA-III) that optimizes the performance metrics of both the algorithm to find a solution for a single problem of machine scheduling. Oscar Castillo et al [25] have given a method to dynamically tune the parameters of ACO to avoid slow or full convergence of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

Ankita¹,

Sahana²

2019

IJISA

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The grid infrastructure has evolved as the integration and collaboration of multiple computer systems, networks, different databases and other network resources. The problem of scheduling in grid environment is an NP complete problem where conventional approaches like First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin Scheduling algorithm (RR), Backfilling is not preferred because of the unexpectedly high computational cost and time in the worst case. Different algorithms, for example bio-inspired algorithms like Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm and Particle Swarm Optimization (PSO) are there which can be applied for solving NP complete problems. Among these algorithms, ACO is designed specifically to solve minimum cost problems and so it can be easily applied in grid environment to calculate the execution time of different jobs. Algorithms have different parameters and the performance of these algorithms extremely depends on the values of its parameters. In this paper, we have proposed a method to tune the parameters of ACO and discussed how parameter tuning affects the performance of ACO which in turn affects the performance of grid environment when applied for scheduling.

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“…However, the surface quality was ignored as an optimization objective in the study. Based on the desirability analysis method, Mobin et al [ 20 ] proposed a parameter optimization method of the evolutionary optimization algorithm. They explored multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA-III) and identified the optimal parameters of the evolutionary optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

Lin

Huang

et al. 2020

Materials

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Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

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A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms

Cited by 39 publications

References 41 publications

Capacitor Voltage Balancing with Online Controller Performance-Based Tuning for a Switch-Sharing-Based Multilevel Inverter

Capacitor Voltage Balancing with Online Controller Performance-Based Tuning for a Switch-Sharing-Based Multilevel Inverter

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

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