Top Research Papers on Optimization Techniques

Recent years have seen a phenomenal rise in the performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Representations from Transformer (BERT), Generative Pretrained Transformer (GPT) and Vision Transformer (ViT), have shown their effectiveness across Natural Language Processing (NLP) and Computer Vision (CV) domains. Transformer-based networks such as ChatGPT have impacted the lives of common men. However, the quest for high predictive performance has led to an exponential increase in transformers' memory and compute fo...

A comparative study of different techniques, first of its kind, is presented to compile classical, intelligent, and quantum-inspired techniques that can be employed in portfolio optimization.

This paper develops a novel nature-inspired metaheuristic technique named snow ablation optimizer (SAO) for numerical optimization and engineering design. The SAO algorithm mainly emulates the sublimation and melting behavior of snow to realize a tradeoff between exploitation and exploration in the solution space and discourage premature convergence. The competitiveness and effectiveness of SAO are validated utilizing 29 typical CEC2017 unconstrained benchmarks and 22 CEC2020 real-world constrained optimization issues which consist of 7 process synthesis and design issues and 15 mechanical eng...

The simulation results indicate that the presented PRBO algorithm performs satisfactorily for all the tested instances, and it can obtain the optimal solution with faster convergence mobility.

The increasing energy prices and pollutants from fossil fuels that threaten the climate, there is a growing preference for renewable energy. The implementation of hybrid renewable energy systems (HRES) has been a challenging task due to its interference, uncertainty, and unpredictable nature. Also, it comes with high net present cost and multi-dimensional architecture facets. It is critical to evaluate HRES using a variety of economic and sizing criteria. This paper aims to provide a succinct review of recent progress in the field of optimization of different HRES using various optimization te...

Bilevel-tailored approaches that exploit these mixed-integer programming techniques to solve bilevel optimization problems, such as pricing or interdiction models that genuinely contain bilinear and thus nonconvex aspects are reviewed.

In order to keep the "good" status of coastal water quality, it is essential to monitor and assess frequently. The Water quality index (WQI) model is one of the most widely used techniques for the assessment of water quality. It consists of five components, with the indicator selection technique being one of the more crucial components. Several studies conducted recently have shown that the use of the existing techniques results in a significant amount of uncertainty being produced in the final assessment due to the inappropriate indicator selection. The present study carried out a comprehensi...

Recently, an increasing research effort has been dedicated to analyze the transmission and dispersion properties of periodic acoustic metamaterials, characterized by the presence of local resonators. Within this context, particular attention has been paid to the optimization of the amplitudes and center frequencies of selected stop and pass bands inside the Floquet–Bloch spectra of the acoustic metamaterials featured by a chiral or antichiral microstructure. Novel functional applications of such research are expected in the optimal parametric design of smart tunable mechanical filters and dire...

There is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations that could be adopted by industry.

A newly developed model of the Sunflower Optimization Algorithm (DSFO) is proposed for minimizing the sum of squared error (SSE) value between the estimated and the actual output voltage of the PEMFC stack.

Simulation results confirm that the usage of the hybrid GWO-PSO techniques causes an observable improvement in a wide scale of the electric power networks behavior.

Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming for researchers, therefore we need efficient optimization techniques. In this systematic review, we explore a range of well used algorithms, including metaheuristic, statistical, sequential, and numerical approaches, to fine-tune CNN hyperparameters. Our research offers an exhaustive categorization of these hyperparameter optimization (HPO) algorithms and in...

This paper presents an organized and concise review of MPPT techniques implemented for the PV systems in literature along with recent publications on various hardware design methodologies.

Expeditious urbanization, population growth, and technological advancements in the past decade have significantly impacted the rise of energy demand across the world. Mitigation of environmental impacts and socio-economic benefits associated with the renewable energy systems advocate the higher integration of the distributed energy systems into the conventional electricity grids. However, the rise of renewable energy generation increases the intermittent and stochastic nature of the energy management problem significantly. Therefore, an optimal energy management technique is required to achiev...

Microgrids (MGs) use renewable sources to meet the growing demand for energy with increasing consumer needs and technological advancement. They operate independently as small-scale energy networks using distributed energy resources. However, the intermittent nature of renewable energy sources and poor power quality are essential operational problems that must be mitigated to improve the MG's performance. To address these challenges, researchers have introduced heuristic optimization mechanisms for MGs. However, local minima and the inability to find a global minimum in heuristic methods create...

The study indicates that the proposed GA edge detection method performs well compared to both classical and fractional-order edge detection methods.

Determination of the optimal input parameters for any of the machining processes plays a pivotal role in achieving the most suitable response values while fulfilling the requirements of both the manufacturers and end users. Among the present-day research community, different multi-criteria decision making (MCDM) techniques have become quite popular as effective multi-objective optimization tools to identify the most appropriate parametric combinations of different machining processes based on real-time experimental data. In this paper, more than 120 research articles (searched through Scienced...

A comprehensive survey of the recent optimization methods employed to enhance DNNs performance on various tasks is presented and the importance of optimization methods in generating the optimal hyper-parameters and structures of Dnns in taking into consideration massive-scale data is analyzed.

CMFD method is proposed using Steerable Pyramid Transform, Grey Level Co-occurrence Matrix and Optimized Naive Bayes Classifier and shows robustness over existing algorithms in the literature even the forged image has undergone many attacks.

This study covers relevant aspects of multi-objective algorithms that which will help the new researchers to apply these algorithms in their research field.

The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation, and the OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).

Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlationa...

Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization...

A Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs and the experimental results show that PAPSO does not violate SLA and outperforms the Power- aware Best Fit Decreasing algorithm (PABFD).

A comprehensive review on the multi-objective optimization technologies in the wind energy forecasting is made, which can simultaneously improve forecasting performance in multiple aspects, such as accuracy and stability.

A systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date comprehends the fundamental concepts, theoretical foundations, and conventional application fields.

Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine ...

An alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA, which demonstrates the high ability to tackle the task scheduling problem and perform better than other methods according to the performance metrics.

The automated techniques are shown to provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole antenna to achieve favorite performance in terms of its two bands.

The proposed technique produces a comprehensive generator cost reduction of the microgrid system and the statistical analysis endorses the improvements of GWOSCACSA over other algorithms presented in the state-of-art-literature.

A new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment and a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process are presented.

A comprehensive review of the efficiency optimization of HE when applied to PPFL, including an overview of algorithmic, hardware, and hybrid optimizations is provided, along with a discussion of their adaptation.

This article captures the evolution of beamforming in the last 25 years from convex to nonconvex optimization and optimization to learning approaches and provides a glimpse into these important signal processing algorithms for a variety of transmit–receive architectures, propagation zones, propagation paths, and multidisciplinary applications.

A Markov decision process model is built to characterize the time series of the uncertainties, and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy.

The need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids is identified.

Simulation results have shown that the optimal system for solving the grid unavailability consists of eighty PVs, two WTs, twenty FCs, forty-one electrolyzers, and one hundred eighteen hydrogen tanks, and it is manifest that the suggested system is economically viable with an LCOE of 0.0628 $/kWh.

This article aims to provide a summary of the latest advances in surface modification techniques, of titanium and its alloys, specifically in biomedical applications, and the microstructure evolution is comprehensively discussed.

The contribution of the researchers is presented by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Partial shading is a serious obstacle to effective utilization of photovoltaic (PV) systems since it result in significant output power reduction. PV array reconfiguration strategy is one of the most efficient used solutions to overcome negative effect caused by the partial shading in PV systems. This paper presents a comprehensive review of the major existing PV array reconfigurations approaches which are used to overcome the problem of partial shading. The different approaches are evaluated and compared according to their techniques, advantages and drawbacks. This work represents an interest...

This paper proposes a new application of Pelican Optimization Algorithm (POA) for optimal Energy Management (EM) in Microgrid (MG) considering Demand Response program (DRP). To maximize the MG operator (MGO) benefit and to reduce the overall operating cost, including the cost of conventional generator fuel and power transaction cost, multi-objective optimization is formulated. To achieve the optimal operation of the MG, a Hybrid DRP is proposed, based on Incentive-based Demand response (IDR) to reduce the peak load and to ensure MG reliability. Reliability is achieved by applying the Hybrid te...