Micro Evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic

Solano-Rojas, Braulio J.; Villalón-Fonseca, Ricardo; Batres, Rafael

doi:10.1016/j.sasc.2023.200057

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…In this study, an artificial neural network model was used for prediction, and a novel and fast version of PSO, named MEPSO, was used to optimize the acquisition function. Compared to PSO, MEPSO showcases reduced computational complexity and a faster convergence rate [26].…”

Section: Research Gap and Contributionmentioning

confidence: 99%

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MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Batres,

Dadras,

Mostafazadeh

et al. 2023

Energies

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A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO’s effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization.

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Section: Research Gap and Contributionmentioning

confidence: 99%

“…The micro evolutionary particle swarm optimization (MEPSO) algorithm is derived from the traditional particle swarm optimization (PSO) algorithm [26]. While MEPSO retains the concept of particles imitating swarm behavior, it introduces a different approach to updating particle positions.…”

Section: Mepsomentioning

confidence: 99%

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Batres,

Dadras,

Mostafazadeh

et al. 2023

Energies

Self Cite

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“…These methods aim to either modify the PSO algorithm itself or integrate it with other metaheuristics. By capitalizing on the strengths of diverse algorithms, these approaches seek to enhance the overall performance of PSO [9].…”

Section: Introductionmentioning

confidence: 99%

Improving with Hybrid Feature Selection in Software Defect Prediction

Pratama,

Herteno,

Faisal

et al. 2024

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Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.

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“…The use of microgrids signifies a significant change in how we manage issues pertaining to energy security, dependability, and the reduction of carbon emissions, as the global energy landscape continues to develop. [1][2][3][4][5] The emergence of microgrid designs is a result of their inherent benefits, such as improved energy dependability, heightened resilience in the face of disturbances, and less reliance on centralized power grids. Furthermore, the incorporation of sustainable energy sources is in line with the overarching objectives of addressing climate change and reducing greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

Vafaeva,

Raju,

Ballabh

et al. 2024

E3S Web Conf.

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This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.

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Micro Evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic

Cited by 7 publications

References 26 publications

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization

Improving with Hybrid Feature Selection in Software Defect Prediction

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

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