Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.

Concise extraction and characterization of the pore-throat network in unconventional hydrocarbon reservoirs: A new perspective

In this study,a new image-based method for the extraction and characterization of pore-throat network for unconventional hydrocarbon storage and exploitation is proposed.“Pore-throat solidity”,which is analogous to particle solidity,and a new method for automatic identification of pores and throats in tight sandstone oil reservoirs are introduced.Additionally,the“pore-throat combination”and“pure pore”are defined and distinguished by drawing the cumulative probability curve of the pore-throat solidity and by selecting an appropriate cutoff point.When the discrete grid set is recognized as a pore-throat combination,Legendre ellipse fitting and minimum Feret diameter are used.When the pore and throat grid sets are identified as pure pores,the pore diameter can be directly calculated.Using the new method,the analytical results for the physical parameters and pore radius agree well with most prior studies.The results comparing the maximum ball and the new model could also prove the accuracy of the latter's in micro and nano scales.The new model provides a more practical theoretical basis and a new calculation method for the rapid and accurate evaluation of the complex processes of oil migration.

Far and Near Optimization:A New Simple and Effective Metaphor-LessOptimization Algorithm for Solving Engineering Applications

In this article,a novel metaheuristic technique named Far and Near Optimization(FNO)is introduced,offeringversatile applications across various scientific domains for optimization tasks.The core concept behind FNO lies inintegrating global and local search methodologies to update the algorithm population within the problem-solvingspace based on moving each member to the farthest and nearest member to itself.The paper delineates the theoryof FNO,presenting a mathematical model in two phases:(i)exploration based on the simulation of the movementof a population member towards the farthest member from itself and(ii)exploitation based on simulating themovement of a population member towards the nearest member from itself.FNO’s efficacy in tackling optimizationchallenges is assessed through its handling of the CEC 2017 test suite across problem dimensions of 10,30,50,and 100,as well as to address CEC 2020.The optimization results underscore FNO’s adeptness in exploration,exploitation,and maintaining a balance between them throughout the search process to yield viable solutions.Comparative analysis against twelve established metaheuristic algorithms reveals FNO’s superior performance.Simulation findings indicate FNO’s outperformance of competitor algorithms,securing the top rank as the mosteffective optimizer across a majority of benchmark functions.Moreover,the outcomes derived by employing FNOon twenty-two constrained optimization challenges from the CEC 2011 test suite,alongside four engineering designdilemmas,showcase the effectiveness of the suggested method in tackling real-world scenarios.

Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats.The foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other seabirds.In this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its prey.The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water.The theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and exploitation.During the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable seabirds.In the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its meal.To evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization process.Comparative studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various metrics.In addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

Identifying the real fracture hidden in rock microcrack zone by acoustic emission energy

Identifying the real fracture of rock hidden in acoustic emission(AE)source clusters(AE-depicted microcrack zone)remains challenging and crucial.Here we revealed the AE energy(representing dissipated energy)distribution rule in the rock microcrack zone and proposed an AE-energy-based method for identifying the real fracture.(1)A set of fracture experiments were performed on granite using wedgeloading,and the fracture process was detected and recorded by AE.The microcrack zone associated with the energy dissipation was characterized by AE sources and energy distribution,utilizing our selfdeveloped AE analysis program(RockAE).(2)The accumulated AE energy,an index representing energy dissipation,across the AE-depicted microcrack zone followed the normal distribution model(the mean and variance relate to the real fracture path and the microcrack zone width).This result implies that the nucleation and coalescence of massive cracks(i.e.,real fracture generation process)are supposed to follow a normal distribution.(3)Then,we obtained the real fracture extension path by joining the peak positions of the AE energy normal distribution curve at different cross-sections of the microcrack zone.Consequently,we distinguished between the microcrack zone and the concealed real fracture within it.The deviation was validated as slight as 1–3 mm.

Improved Harris Hawks Algorithm and Its Application in Feature Selection

This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lack of thorough exploitation depth.To tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators.The effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test functions.Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities.Additionally,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional datasets.Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets.The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature selection.Furthermore,IHHO-FS shows strong competitiveness relative to numerous algorithms.

Hydrochemical characteristics of surface water in Hengduan mountain region of Eastern Tibet and its response to human activities:A case study of Duoqu Basin,Jinsha River

The analysis of hydrochemical characteristics and influencing factors of surface river on plateau is helpful to study water hydrological cycle and environmental evolution,which can scientifically guide rational development and utilization of water resources and planning of ecological environment protection.With the expansion and diversification of human activities,the quality of surface rivers will be more directly affected.Therefore,it is of great significance to pay attention to the hydrochemical characteristics of plateau surface rivers and the influence of human activities on their circulation and evolution.In this study,surface water in the Duoqu basin of Jinsha River located in Hengduan mountain region of Eastern Tibet was selected as the representative case.Twenty-three groups of surface water samples were collected to analyze the hydrochemical characteristics and ion sources based on correlation analysis,piper trigram,gibbs model,hydrogen and oxygen isotopic techniques.The results suggest the following:(1)The pH showed slight alkalinity with the value ranged from 7.25 to 8.62.Ca^(2+),Mg^(2+)and HCO_(3)^(–)were the main cations and anions.HCO_(3)^(-)Ca and HCO_(3)^(-)Ca·Mg were the primary hydrochemical types for the surface water of Duoqu River.The correlation analysis showed that TDS had the most significant correlation with Ca^(2+),Mg^(2+)and HCO_(3)^(–).Analysis on hydrogen and oxygen isotopes indicated that the surface rivers were mainly recharged by atmospheric precipitation and glacial melt water in this study area.(2)The surface water had a certain reverse cation alternating adsorption,and surface water ions were mainly derived from rock weathering,mainly controlled by weathering and dissolution of carbonates,and secondly by silicates and sodium rocks.(3)The influence of human activities was weak,while the development of cinnabar minerals had a certain impact on the hydrochemistry characteristics,which was the main factor for causing the increase of SO_(4)^(2–).The densely populated county towns and temples with frequent incense burning activities may cause some anomalies of surface water quality.At present,the Duoqu River watershed had gone through a certain influence of mineral exploitation,so the hydrological cycle and river eco-environment at watershed scale will still bound to be change.The results could provide basic support for better understanding water balance evolution as well as the ecological protection of Duoqu River watershed.

Impact of well placement and flow rate on production efficiency and stress field in the fractured geothermal reservoirs

Geothermal energy has gained wide attention as a renewable alternative for mitigating greenhouse gas emissions.The advancements in enhanced geothermal system technology have enabled the exploitation of previously inaccessible geothermal resources.However,the extraction of geothermal energy from deep reservoirs poses many challenges due to high‐temperature and high‐geostress conditions.These factors can significantly impact the surrounding rock and its fracture formation.A comprehensive understanding of the thermal–hydraulic–mechanical(THM)coupling effect is crucial to the safe and efficient exploitation of geothermal resources.This study presented a THM coupling numerical model for the geothermal reservoir of the Yangbajing geothermal system.This proposed model investigated the geothermal exploitation performance and the stress distribution within the reservoir under various combinations of geothermal wells and mass flow rates.The geothermal system performance was evaluated by the criteria of outlet temperature and geothermal productivity.The results indicate that the longer distance between wells can increase the outlet temperature of production wells and improve extraction efficiency in the short term.In contrast,the shorter distance between wells can reduce the heat exchange area and thus mitigate the impact on the reservoir stress.A larger mass flow rate is conducive to the production capacity enhancement of the geothermal system and,in turn causes a wider range of stress disturbance.These findings provide valuable insights into the optimization of geothermal energy extraction while considering reservoir safety and long‐term sustainability.This study deepens the understanding of the THM coupling effects in geothermal systems and provides an efficient and environmentally friendly strategy for a geothermal energy system.

The Damaging Effects of Abstracting the Deep Aquifers’Groundwater in Jordan-Quality Constraints

The deep aquifers in Jordan contain non-renewable and fossil groundwater and their extraction is quasi a mining process, which ends in the depletion of these resources. Although aquifers in the majority of groundwater basins in Jordan are vertically and horizontally interconnected stratification in different water quality horizons with generally increasing water salinity with the depth is observed. Many officials and planners advocate the extraction of deep salty and brackish water to be desalinated and used in household, industrial, and agricultural uses. In this article, the quality of the groundwater in the different deep aquifers and areas in Jordan is discussed. The results of this study show that the consequences of the deep groundwater exploitation are not restricted to depletion of the deep aquifers but also that the overlying fresh groundwater will, due to vertical and horizontal interconnectedness of the different aquifers, percolate down to replace the extracted deep groundwater. This will cause the down-percolating fresh groundwater to become salinized in the deep saline aquifers, which means that extracting the deep brackish and saline groundwater is not only an emptying process of the deep groundwater but also it is an emptying process of the fresh groundwater overlying them. The results allow to conclude that any extraction of the deep groundwater in areas lying to the north of Ras en Naqab Escarpment will have damaging impacts on the fresh groundwater in the overlying fresh groundwater aquifers. This article strongly advises not to extract the deep brackish and saline groundwater, but to conserve that groundwater as a base supporting the overlying fresh groundwater resources, and that will help in protecting the thermal mineralized water springs used in spas originating from these deep aquifers. The increasing water needs of the country can be covered by the desalination of seawater at Aqaba, which is the only viable option for Jordan at present and in the coming decades.

Skill Optimization Algorithm:A New Human-Based Metaheuristic Technique

Metaheuristic algorithms are widely used in solving optimization problems.In this paper,a new metaheuristic algorithm called Skill Optimization Algorithm(SOA)is proposed to solve optimization problems.The fundamental inspiration in designing SOA is human efforts to acquire and improve skills.Various stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual effort.The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal types.The optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization problems.In addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach.Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.

Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

Novel concepts of mechanical technology for gas recovery from marine hydrate reservoir

According to the characteristics of marine natural gas hydrate,China has proposed the solid-state fluidization exploitation technology or natural gas hydrate,with subsea exploitation being key to the commercial recovery of gas.In this paper,two new integrated tools are proposed for breaking and collecting natural gas hydrate,and their working principles and steps are illustrated.Finite element analysis,three-dimensional modeling,and simulations were conducted for both exploitation tools to verify their technological feasibility.The results show that the two exploitation tools can effectively improve the efficiency of hydrate exploitation and ensure the stability of the hydrate reservoir.This provides a reference for further research on the solid-state fluidization exploitation technology of marine gas hydrates.

Migration Algorithm:A New Human-BasedMetaheuristic Approach for Solving Optimization Problems

This paper introduces a newmetaheuristic algorithmcalledMigration Algorithm(MA),which is helpful in solving optimization problems.The fundamental inspiration of MA is the process of human migration,which aims to improve job,educational,economic,and living conditions,and so on.Themathematicalmodeling of the proposed MAis presented in two phases to empower the proposed approach in exploration and exploitation during the search process.In the exploration phase,the algorithm population is updated based on the simulation of choosing the migration destination among the available options.In the exploitation phase,the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions.MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite.In addition,MA’s results are compared with the performance of twelve well-known metaheuristic algorithms.The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems.The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions.Also,the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.

Experimental study on solid particle migration and production behaviors during marine natural gas hydrate dissociation by depressurization

Sand production is one of the main obstacles restricting gas extraction efficiency and safety from marine natural gas hydrate(NGH)reservoirs.Particle migration within the NGH reservoir dominates sand production behaviors,while their relationships were rarely reported,severely constrains quantitative evaluation of sand production risks.This paper reports the optical observations of solid particle migration and production from micrometer to mesoscopic scales conditioned to gravel packing during depressurization-induced NGH dissociation for the first time.Theoretical evolutionary modes of sand migration are established based on experimental observations,and its implications on field NGH are comprehensively discussed.Five particle migration regimes of local borehole failure,continuous collapse,wormhole expansion,extensive slow deformation,and pore-wall fluidization are proved to occur during depressurization.The types of particle migration regimes and their transmission modes during depressurization are predominantly determined by initial hydrate saturation.In contrast,the depressurization mainly dominates the transmission rate of the particle migration regimes.Furthermore,both the cumulative mass and the medium grain size of the produced sand decrease linearly with increasing initial methane hydrate(MH)saturation.Discontinuous gas bubble emission,expansion,and explosion during MH dissociation delay sand migration into the wellbore.At the same time,continuous water flow is a requirement for sand production during hydrate dissociation by depressurization.The experiments enlighten us that a constitutive model that can illustrate visible particle migration regimes and their transmission modes is urgently needed to bridge numerical simulation and field applications.Optimizing wellbore layout positions or special reservoir treatment shall be important for mitigating sand production tendency during NGH exploitation.

Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

In this paper,based on the concept of the NFL theorem,that there is no unique algorithm that has the best performance for all optimization problems,a new human-based metaheuristic algorithm called Language Education Optimization(LEO)is introduced,which is used to solve optimization problems.LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules.LEO is mathematically modeled in three phases:(i)students selecting their teacher,(ii)students learning from each other,and(iii)individual practice,considering exploration in local search and exploitation in local search.The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal,multimodal types and the CEC 2017 test suite.The optimization results show that LEO,with its acceptable ability in exploration,exploitation,and maintaining a balance between them,has efficient performance in optimization applications and solution presentation.LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms.Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior andmore competitive in combating the compared algorithms.The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.

Billiards Optimization Algorithm:A New Game-Based Metaheuristic Approach

Metaheuristic algorithms are one of themost widely used stochastic approaches in solving optimization problems.In this paper,a new metaheuristic algorithm entitled Billiards Optimization Algorithm(BOA)is proposed and designed to be used in optimization applications.The fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards game.Various steps of BOA are described and then its mathematical model is thoroughly explained.The efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal,high-dimensional multimodal,and fixed-dimensionalmultimodal functions.In order to analyze the quality of the results obtained by BOA,the performance of the proposed approach is compared with ten well-known algorithms.The simulation results show that BOA,with its high exploration and exploitation abilities,achieves an impressive performance in providing solutions to objective functions and is superior and far more competitive compared to the ten competitor algorithms.

Thermal conductive proppant with self-suspension ability

The in situ mining technology is applied to the exploitation of medium-and low-maturity shale oil,which can use heaters to warm up the oil shale formations and pyrolyze the kerogen.Due to the low thermal conductivity of oil shale,electric heaters need extra equipment to improve heat transfer efficiency.In this study,a thermally conductive proppant is fabricated by coating epoxy-resin and graphite on ceramic proppants for the first time,which could support the fracturing crack and transfer heat.The thermal conduction property of epoxy-resin and graphite coated proppants(EGPs)is 245%higher than that of uncoated proppants,which can transfer more heat to the oil shale formation and accelerate the conversion of kerogen.The adhesive property of EGPs is improved by 47.9%under the load force of 1500 nN,which prolongs the time for the fracture to close.In summary,this novel proppant is expected to assist in-situ mining technology in the production of medium and low-maturity shale oil.

An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider interest in solving optimization problems.However,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN deteriorate.This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN.Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms.Unlike the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation characteristics.In addition,the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality,fitness,and convergence speed of the original RUN.Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original RUN.To promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness.Simulation results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics.Therefore,with its higher computation efficiency,Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.