Hybrid metaheuristic algorithms: a recent comprehensive review with bibliometric analysis

Metaheuristic algorithms are widely used in various applications. Collaborating two or more algorithms in a hybrid form has shown great improvements in terms of the algorithm's performance. This paper highlights the recently published work during the last decade from a quantitative perspective. The biometric measures include the number of publications, citations, average citations per publication, h-index, and field-weighted citation impact (FWCI) based on the data extracted from the Scopus database. Statistical measures, co-occurrence and co-authorship maps, and illustrative graphs have been implemented using software tools. According to the collected data, about 3469 articles have been published during the last decade with an increasing rate of 44.1 papers per year. Most of these articles have been published as journal articles with a percentage of 68.3%, followed by conference articles occupied 29.5%. China, India and Iran contributed the largest number of articles at 1076, 965, and 239, respectively. Parouha, Verma, and Kamel, are the top-ranked authors with 14, 10, and 9 publications, respectively. The most areas of interest are computer science, engineering and mathematics with publication percentages of 27.69%, 25.55% and 13.91%, respectively. The data presented in this paper gives the researchers a clear image of this hot topic to start new research

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field

Metaheuristic-Based Algorithms for Optimizing Fractional-Order Controllers—A Recent, Systematic, and Comprehensive Review

Metaheuristic optimization algorithms (MHA) play a significant role in obtaining the best (optimal) values of the system’s parameters to improve its performance. This role is significantly apparent when dealing with systems where the classical analytical methods fail. Fractional-order (FO) systems have not yet shown an easy procedure to deal with the determination of their optimal parameters through traditional methods. In this paper, a recent, systematic. And comprehensive review is presented to highlight the role of MHA in obtaining the best set of gains and orders for FO controllers. The systematic review starts by exploring the most relevant publications related to the MHA and the FO controllers. The study is focused on the most popular controllers such as the FO-PI, FO-PID, FO Type-1 fuzzy-PID, and FO Type-2 fuzzy-PID. The time domain is restricted in the articles published through the last decade (2014:2023) in the most reputed databases such as Scopus, Web of Science, Science Direct, and Google Scholar. The identified number of papers, from the entire databases, has reached 850 articles. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied to the initial set of articles to be screened and filtered to end up with a final list that contains 82 articles. Then, a thorough and comprehensive study was applied to the final list. The results showed that Particle Swarm Optimization (PSO) is the most attractive optimizer to the researchers to be used in the optimal parameters identification of the FO controllers as it attains about 25% of the published papers. In addition, the papers that used PSO as an optimizer have gained a high citation number despite the fact that the Chaotic Atom Search Optimization (ChASO) is the highest one, but it is used only once. Furthermore, the Integral of the Time-Weighted Absolute Error (ITAE) is the best nominated cost function. Based on our comprehensive literature review, this appears to be the first review paper that systematically and comprehensively addresses the optimization of the parameters of the fractional-order PI, PID, Type-1, and Type-2 fuzzy controllers with the use of MHAs. Therefore, the work in this paper can be used as a guide for researchers who are interested in working in this field

The Role of Random Walk-Based Techniques in Enhancing Metaheuristic Optimization Algorithms—A Systematic and Comprehensive Review

Metaheuristic algorithms (MHAs) occupy considerable attention among researchers because of their high performance and robustness in optimizing several engineering problems. Random walk (RW) techniques showed a significant role in improving the performance of these algorithms. Therefore, this paper aims to provide a systematic and comprehensive review of the role of three substantial random-walk (RW) strategies in enhancing the performance of MHAs. These strategies are the Gaussian, Levy Flight and Quantum random walks. The PRISMA methodology is applied through the articles obtained from four famous scientific databases. The study provides the integration mechanisms as well as the best controlling parameters’ values while integrating these RW strategies into Particle Swarm Optimization (PSO) to produce the Gaussian PSO (GPSO), Levy Flight PSO (LFPSO) and Quantum PSO (QPSO). An experimental study has been conducted to assess the performances of these algorithms in addition to the standard PSO on 23 unimodal, multimodal and fixed-dimension multimodal benchmark functions. Statistical measures have been calculated based on 30-run optimization processes. The comparisons showed that the QPSO, LFPSO, GPSO and PSO have successfully reached the optimal values of 23 standard benchmark functions with average percentages of 65%, 31%, 13% and 11%, respectively. Accordingly, the QPSO has gained the outstanding rank, especially for unimodal and multimodal functions followed by the LFPSO while the standard PSO comes in the last position preceded by the GPSO. From the results, it can be concluded that integrating random walk strategies into existing or new metaheuristic algorithms is capable of enhancing the optimization process and hence provides reliable results when applied to engineering applications

Meteorological Water Balance of Khan Al-Baghdadi and surrounding area within Anbar Governorate – West of Iraq.

The water balance equation for any natural area or water body indicates the relative values of inflow, outflow and change in water storage for the area or water body. Analysis of watersheds and drainage patterns of the studied area plays a vital role in understanding the hydrogeological behavior and expressing the prevailing climate, geomorphology and structural antecedents of terrains. This research will study the climate characteristics and meteorological water balance to calculate runoff and groundwater recharge as water surplus. The research used the meteorological parameters recorded in Hit station during the period (1995-2010) to describe climate conditions as well as calculating water surplus using Thorntwaite formula. The climate of area classified as hot dry in summer and cold low rainfall in winter as a continental semi-dry to dry climate class. The calculated potential evapotranspiration was (1174.3) mm, while calculated actual evapotranspiration was (69.59) mm. The amount of water surplus was (31.71) mm, divided into (20.26) mm as runoff and (11.45) mm as groundwater recharge. The average annual of groundwater recharge in the area was (72,020,500) cubic meters. The estimated percent of the water surplus and deficit from the annual rainfall was (31.3%) and (68.69%) respectively

Cytotoxicity of nickel zinc ferrite nanoparticles on cancer cells of epithelial origin

In this study, in vitro cytotoxicity of nickel zinc (NiZn) ferrite nanoparticles against human colon cancer HT29, breast cancer MCF7, and liver cancer HepG2 cells was examined. The morphology, homogeneity, and elemental composition of NiZn ferrite nanoparticles were investigated by scanning electron microscopy, transmission electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The exposure of cancer cells to NiZn ferrite nano-particles (15.6-1,000 μg/mL; 72 hours) has resulted in a dose-dependent inhibition of cell growth determined by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The quantification of caspase-3 and -9 activities and DNA fragmentation to assess the cell death pathway of the treated cells showed that both were stimulated when exposed to NiZn ferrite nanoparticles. Light microscopy examination of the cells exposed to NiZn ferrite nanoparticles demonstrated significant changes in cellular morphology. The HepG2 cells were most prone to apoptosis among the three cells lines examined, as the result of treatment with NiZn nanoparticles. In conclusion, NiZn ferrite nanoparticles are suggested to have potential cytotoxicity against cancer cells

Thermophysical properties of graphene-based nanofluids

Heat transfer operations are very common in the process industry to transfer a huge amount of thermal energy, i.e., heat, from one fluid to another for different purposes. Many fluids are used as heat transfer fluid (HTF), in which water is the most common HTF due to its high specific heat, availability, and affordability. However, conventional HTFs, including water, have a lower thermal conductivity, which is the most critical thermophysical property, hence decreased heat transfer efficiency. The addition of solid particles of highly thermally conductive material, specifically at nano-size, i.e., nanoparticles NPs, result in nanofluid NF, which has evolved over the last two decades as efficient HTF and have been investigated in a wide range of applications. Among NPs, graphene (Gr) based materials have shown very high potential as NF due to the very high thermal conductivity up to 5,000 W/m.K, hence higher thermal conductivity NF. This work aims to thoroughly discuss the thermophysical properties of Gr-based NFs, including thermal conductivity, heat capacity, density, and viscosity. The discussion focus on the thermophysical properties as it is the ultimate determinator of the heat transfer characteristics of the HTF, such as the convective and the overall heat transfer coefficient as well as the heat transfer capacity of the NF. The discussion expands to the relative enhancement in such thermophysical properties reaching up to a 40% increase in thermal conductivity, as the most critical thermophysical property. The discussion shows that Gr-based NF has a much higher thermal conductivity relative to widely studied metal oxide NF and at much lower content, and lower density and viscosity increase, which is critical for determining the pumping power requirements. Critical challenges facing the application of Gr-based NFs such as cost, stability, increased density and viscosity, and environmental impacts are thoroughly discussed with mitigation recommendations given

A Predictive model for liver disease progression based on logistic regression algorithm

Liver disease counts to be one of the most prevalent diseases in the worldwide. Therefore, this paper is aim to address the problem of predicting liver disease progression. As the existing predictive models focus on predicting the label of disease; the probability of developing the disease is still obscure. This paper, therefore, has proposed a model to predict the probability occurrence of liver diseases. The proposed predictive model used logistic regression abilities to predict the probability of liver disease occurrence. ILPD dataset was used to analyze the performance of the model. The predictive model has shown outstanding performance with a prediction accuracy rate of 72.4%, the sensitivity of 90.3%, the specificity of 78.3 %, Type I Error of 9.7 %, Type II Error of 21.7 %, and ROC of 0.758%. The model has furthermore confirmed the feasibility of the laboratory tests such as as (Age; Direct Bilirubin (DB), Alamine_Aminotransferase (SGPT), Total_Protiens (TP), Albumin (ALB)) to predict the disease progression. The predictive model will be helpful to patients and doctors to realize the progression of the disease and make a suitable timely intervention

MECHANICAL PROPERTIES AND THERMAL BEHAVIOUR OF TWO-STAGE CONCRETE CONTAINING PALM OIL FUEL ASH

ABSTRACT: Two-stage concrete (TSC) is a special type of concrete which is made by placing coarse aggregate in a formwork and injecting a grout either by pump or under the gravity force to fill the voids. Over the decades, the application of supplementary cementing materials in conventional concrete has become widespread, and this trend is expected to continue in TSC as well. Palm oil fuel ash (POFA) is one of the ashes which has been recognized as a good pozzolanic material. This paper presents the experimental results on the performance behaviour of POFA in developing physical and mechanical properties of two-stage concrete. Four concrete mixes namely, TSC with 100% OPC as a control, and TSC with 10, 20 and 30% POFA were cast, and the temperature growth due to heat of hydration and heat transfer in the mixes was recorded. It has been found that POFA significantly reduced the temperature rise in two-stage aggregate concrete and delayed the transfer of heat to the mass of concrete. The compressive and tensile strengths, however, increased with the replacement of up to 20% POFA. The results obtained and the observation made in this study suggest that the substitution of OPC by POFA is beneficial, particularly for prepacked mass concrete where thermal cracking due to extreme heat rise is of great importance