Optimization Goal Research Articles

Simulation Analysis and Experimental Study on Airfoil Optimization of Low-Velocity Turbine

By combining computational fluid dynamics (CFD) and surrogate model method (SMM), the relationship between turbine performance and airfoil shape and flow characteristics at low flow rate is revealed. In this paper, the flow velocity tidal energy airfoil model is designed based on the Kriging model, and the original airfoil with a relative thickness of 12% and a relative curvature of 2.5% is obtained. The parameter optimization is carried out by setting the 4th CST equations through the surrogate model; the maximum lift-drag ratio is the optimization goal, the optimization design variable is 10, the maximum number of iterations is 100, and the maximum number of sub-optimization iterations is 200. The results show that the hydrodynamic performance of the airfoil with thinner thickness and more curvature is better, the maximum thickness part is shifted forward by 4.58%, and the lift-drag ratio is improved by 4.03%. The flow field and the efficiency are more stable, which provides an engineering reference for the optimal design of hydraulic turbine airfoils under low flow velocity. It supplements the research on the performance of turbine blades in low velocity.

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform.

Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients. A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached. This work demonstrates that the H-DBTL approach, combined with a robot, develops a novel paradigm for the on-demand optimization of functional materials.

Research on the village layout optimization in China's developed areas based on daily life circles.

Public service facilities are the basic material carrier of social services. It is of great significance for the operation of social justice and the improvement of residents' wellbeing. In the process of rapid urbanization, the demand for traffic convenience and diversity of public service facilities in rural areas has been further improved. Since the sudden outbreak of the COVID-19 epidemic, it is more necessary to meet the public service needs of human daily life within a certain space. The huge gap between urban and rural infrastructures, human living conditions, and supporting public services such as education, health, and culture has become a key bottleneck constraining the integration of urban and rural areas. It is appropriate to focus on the requirements for the construction of daily life circles and prioritize the satisfaction of villagers' increasing demand for public services in regions characterized by high levels of urban-rural integration. The behavior preference of public service facilities significantly affects the choice of residential areas. The existing research on village layout optimization focused on the spatial pattern, landscape morphology, influencing factors, and other contents. To some extent, the preference for equalization of public services in the context of urban-rural integration has been ignored. Uneven distribution and low utilization of public service facilities in villages resulted in an inability to adapt spatially and functionally in response to the renewal of urban-rural relations. Taking a highly developed urban and rural integration area named Wuxi City of Jiangsu Province in China as an example, this paper combines the current situation of public service facility allocation and the transport situation of residents in rural areas to build a life-circle system of different levels. On this basis, combined with the optimization goal of public service facilities and the constraints of agglomeration coverage, we try to determine the village layout optimization scheme under the construction of both the daily life circle and location-allocation model. The study can effectively adjust the allocation of public resources in rural areas and solve the problems of irrational village layout leading to long travel distances. It can also serve as a reference for improving the situation of lagging rural public service functions and promoting urban-rural equalization.

A teamwork OSCE station – Encompassing shared decision making between a doctor, pharmacist and patient

Background and purposeTeamwork with a focus on incorporating interprofessional education (IPE) is a core skill taught in pharmacy curriculum. Including the patient in shared decision making has become a more empathetic and holistic approach to care. Teamwork skills and approaches are usually assessed by peers via group work. Educational activity and settingA teamwork Objective Structured Clinical Examination (OSCE) station was implemented in 2022 in two countries, conducted in final year pharmacy students where students had to solve a clinical scenario with a doctor and patient in the room and manage any resistances to therapy recommendations. Students were marked on their collaborative skills and how they articulated clinical evidence to justify their therapy recommendations to reach an optimal goal to suit the team. Perception data from students simulated patients and doctors were also collected. FindingsAcross both countries (n = 414), the overall mean score was 79.1% (24.0%SD) across six different OSCE cases. Students generally struggled with the concept of engaging in a two-way conversation, and often presented their full recommendations without any breaks to confirm understanding or agreement. Simulated patients appreciated the OSCE showing a patient role in decision making. Simulated doctors valued the OSCE for its assessment of clinical justification in times of disagreement. Overall, 86.2% of students (n = 354) agreed or strongly agreed the station assessed their teamwork skills adequately. SummaryA teamwork OSCE station is a valuable assessment for assessing shared decision making skills and more work around prepare students to engage in two-way conversations is needed.

A Vision for Global CT Radiation Dose Optimization

The topic of CT radiation dose management is receiving renewed attention since the recent approval by CMS for new CT dose measures. Widespread variation in CT dose persists in practices across the world, suggesting that current dose optimization techniques are lacking. The author outlines a proposed strategy for facilitating global CT radiation dose optimization. CT radiation dose optimization can be defined as the routine use of CT scan parameters that consistently produce images just above the minimum threshold of acceptable image quality for a given clinical indication, accounting for relevant patient characteristics, using the most dose-efficient techniques available on the scanner. To accomplish this, an image quality–based target dose must be established for every protocol; for nonhead CT applications, these target dose values must be expressed as a function of patient size. As variation in outcomes is reduced, the dose targets can be decreased to more closely approximate the minimum image quality threshold. Maintaining CT radiation dose optimization requires a process control program, including measurement, evaluation, feedback, and control. This is best accomplished by local teams made up of radiologists, medical physicists, and technologists, supported with protected time and needed tools, including analytics and protocol management applications. Other stakeholders critical to facilitating CT radiation dose management include researchers, funding agencies, industry, regulators, accreditors, payers, and the ACR. Analogous coordinated approaches have transformed quality in other industries and can be the mechanism for achieving the universal goal of CT radiation dose optimization.

Quality Of E-Ktp Services In The Office Bintaro District District Administrative City Reservation South Jakarta Dki Jakarta Province

Public service, as outlined by Law no. 25 of 2009 and regional autonomy policies, serves as a cornerstone of governmental functions. This research delves into challenges faced by the Bintaro Village Office in providing Electronic Identity Card (E-KTP) services, employing a qualitative approach. Despite positive E-Government transformations, practical hurdles persist, with the ambiguity surrounding service timelines being a significant issue. The study scrutinizes dimensions such as service attention, facilities, infrastructure, and employee appearance, utilizing a phenomenological approach. The research reveals variations in service attention, emphasizing the need for consistent improvement efforts. Tangible evidence indicates adequate facilities but highlights staff shortages impacting administrative services. While employee appearance demonstrates positive attributes, refinement through training in interpersonal skills is recommended. The comprehensive set of recommendations includes increasing staff, training initiatives, facility maintenance, technology adoption, enhanced communication, periodic community satisfaction measurement, rewards and sanctions, transparent information provision, continuous monitoring, complaint system improvement, increased accountability, and service guidelines update. In conclusion, this research serves as a strategic guide for the Bintaro Subdistrict Office, aiming to enhance service quality, meet community needs, and achieve optimal and sustainable service goals.

P922 A multicenter retrospective real-world study to determine the optimal cases of vedolizumab-treated UC patients

Abstract Background Vedolizumab (VDZ) is a gut-selective monoclonal antibody approved for ulcerative colitis (UC). This real-world study aimed to evaluate the effectiveness of VDZ in the treatment of UC, with the goal of further treatment optimization. Methods In this multicenter, retrospective, observational chart review, 370 patients with UC who received at least one dose of VDZ in clinical practice at 16 tertiary hospitals in Japan were enrolled. Comprehensive analyses of predictors of 2- and 54-week remission, drug persistence, and the development of loss of response were performed. Results Lower partial Mayo scores and lower Mayo endoscopic subscores were significantly associated with 2-week remission in the multivariate analysis. More 2-week remitters achieved clinical remission at Week 52 than non-2-week remitters. Clinical remission rates were significantly higher in biologic-naïve than in biologic-non-naïve patients across Weeks 2 to 54. Higher clinical remission rates at Week 54 were significantly associated with lower baseline disease severity and no prior anti-tumor necrosis factor alpha (anti- TNFα) treatment. Among patients with prior anti-TNFα treatment, longer duration of anti-TNFα treatment was associated with higher clinical remission rates at Week 54 (28.1%, 32.7%, and 60.0% for ≤3 months, >3 to <12 months, and ≥12 months, respectively, p=0.001 between groups). In addition, clinical remission rate at Week 54 was higher in patients who had loss of response to anti-TNFα treatment than in those who had primary nonresponse (54.4% vs 24.6%; p=0.001). The 54-week persistence rate was 64.5%. Concomitant use of tacrolimus (OR 2.76, 95% CI 1.00, 7.62, p=0.05) and shorter disease duration (median:7.8 years), (OR 0.33, 95% CI 0.13, 0.82 for ≥7.8 years vs <7.8 years p=0.017) were associated with loss of response to VDZ in the multivariate analysis, but prior use of anti- TNFα treatment was not. Conclusion This retrospective study confirms favorable outcomes with VDZ in patients without prior exposure to anti-TNFα treatment. However, among patients with history of anti-TNFα- exposure, better outcomes were observed among those who experienced loss of response than those with primary nonresponse, as well as those who had a longer duration of anti-TNFα treatment.

Dose Optimization for Novel Oncology Agents: Design Options and Strategies

Over the past decade, drug development in oncology has shifted from cytotoxic agents to drugs with new mechanisms of action, such as cancer immunotherapies, targeted therapeutics, T-cell engagers and others. The conventional maximum tolerated dose (MTD) based dose-finding paradigm is not suitable for the development of these new agents. Further, health authorities, especially the FDA, are requesting more thorough dose optimization prior to the initiation of pivotal trials, and initiatives such as the FDA’s project Optimus have been launched to accelerate this paradigm shift. Dose optimization is more complicated than finding the MTD and requires consideration of complex mechanisms of action, schedule optimization, long-term drug tolerability, and possibly novel pharmacodynamic endpoints. Thus, thoughtful study designs, translational data, and statistical modeling play an increasingly important role to achieve the goal of dose optimization. This article captures opinions from the 2022 ASA biophamaceutical section regulatory-industry statistics workshop session “Dose finding and optimization for novel oncology agents—the new challenges and novel technologies.” We present general design options for dose optimization. Pros and cons of these design options are discussed, and a real-world case study is provided to illustrate a strategy of dose optimization. Discussions focused on practical considerations are included.

Geography Teachers' Perceptions of Various Uses of Learning Media in Public Senior High Schools in South Tangerang City

Education is a very important and inseparable part of the process of preparing quality human resources. Education is also a conscious effort to develop the potential of human resources. To achieve optimal educational goals, there needs to be a positive interaction between the community, parents and the school. The development of technology has had a significant impact on education, with the accessibility of information made easier by the internet and digital resources, changing the way we learn and access knowledge. In this study, the main objective is to analyze geography teachers' perceptions of the various uses of learning media in public high schools in South Tangerang City. This research is descriptive research using the survey method. Data analysis was conducted using descriptive statistical analysis methods, specifically percentage analysis. By using percentages, this study can analyze the level of geography teachers' perceptions of the various uses of learning media in public high schools in South Tangerang City. The results showed that the perception of geography teachers in public high schools in South Tangerang City towards the use of various learning media is in the very good category, with a percentage reaching 94%. This finding shows that geography teachers in public high schools in South Tangerang City have a very positive perception of the use of learning media.

Interventions aimed to increase average 24-h systolic blood pressure reduce blood pressure drops in patients with reflex syncope and orthostatic intolerance.

Systolic blood pressure (SBP) drops recorded by 24-h ambulatory blood pressure (BP) monitoring (ABPM) identify patients with susceptibility to reflex syncope and orthostatic intolerance. We tested the hypothesis that treatments aimed to increase BP (reassurance, education, and lifestyle measures plus pharmacological strategies) can reduce SBP drops. This was a multicentre, observational proof-of-concept study performed in patients with reflex syncope and/or orthostatic intolerance and with SBP drops on a screening ABPM. Among 144 eligible patients, 111 underwent a second ABPM on average 2.5 months after start of treatment. Overall, mean 24-h SBP increased from 114.1 ± 12.1 to 121.4 ± 14.5 mmHg (P < 0.0001). The number of SBP drops <90 and <100 mmHg decreased by 61%, 46% during daytime, and by 48% and 37% during 24-h period, respectively (P < 0.0001 for all). The dose-response relationship between difference in 24-h average SBP increase and reduction in number of SBP drops reached a plateau around ∼15 mmHg increase of 24-h SBP. The reduction in SBP drop rate was consistent and significant in patients who underwent deprescription of hypotensive medications (n = 44) and in patients who received BP-rising drugs (n = 67). In patients with reflex syncope and/or orthostatic intolerance, an increase in average 24-h SBP, regardless of the implemented strategy, significantly reduced the number of SBP drops and symptom burden. A 13 mmHg increase in 24-h SBP appears to represent the optimal goal for aborting the maximal number of SBP drops, representing a possible target for future interventions. ClincalTrials.gov identifier: NCT05729724.

Optimization of rheological and mechanical properties of sustainable lateritic self-compacting concrete containing sisal fiber using response surface methodology

This study aims to identify effective methods for utilizing the percentage of waste laterite scraps as coarse aggregate through the optimization of performance-based sustainable lateritic self-compacting concrete. Thirteen mixes were made using RSM's central composite design (CCD), each with a unique combination of input factors (LA: 10–50 %, SF: 0.25–1.25 %), which were then tested for six responses (rheological properties: Slump flow (SF), V-funnel time (VT), and T500 strength characteristics: compressive strength, split tensile strength, and flexural strength). The ANOVA method was employed to evaluate the precision of the mathematical models created for the experiment. ANOVA was used to design and verify six response models (five quadratic and one linear). The analysis of variance results demonstrated the validity of each constructed model. Multi-objective optimization was used to study the interactions between the different components and determine the appropriate mixing ratio, resulting in an optimal percentage of LA and SF of the required characteristics without compromising the rheological and mechanical properties. Considering the goals of multi-objective optimization, the optimal LSCC produced with SF addition of 0.76 % and LA as a granite aggregate replacement of 26.8 %, yielding fresh properties of VT of 9.06 s, T500 of 2.26 s, and slump flow of 795.63 mm, and hardened properties of CS of 49.24 MPa, FS of 5.21 MPa and STS of 4.54 MPa. Experimental validation was carried out with an error rate of less than 5 % of the anticipated optimal data.

Experimental evaluation and statistical modeling of sustainable sisal fiber-reinforced fly ash-based lateritic concrete using response surface methodology

This research uses waste laterite scraps as coarse aggregates in sisal fiber-reinforced fly ash-based lateritic concrete and evaluates its strength properties to create sustainable concrete. By lowering the requirement for virgin materials, its use lessens the need for granite mining, environmental contamination, and the preservation of natural resources. Using response surface methodology (RSM’s) central composite design (CCD), twenty-nine different lateritic concrete mixtures have been designed with varying input factor combinations (LA: 25%–100%, FA: 5%–20%, SF: 0.25%–1%, and SP: 1%–1.6%) with a total of 279 test specimens were cast and tested for responses like compressive, split-tensile, and flexural strengths at 28 days. The analysis of variance (ANOVA) test was performed to determine the accuracy of the mathematical models developed following the experimental results. Quadratic models were proposed due to a higher coefficient of determination (R2) for all responses. The higher addition of laterite aggregate linearly reduces fresh concrete’s workability. The compressive strength of the M30 grade concretes for 28 days increased by 11.20% when 25% of the granite aggregates were substituted with laterite aggregates. Compressive strength decreases above 25% replacement. Similarly, split tensile and flexural strengths are increased over the control mix by 10.86% and 5.88%, respectively. Multi-objective optimization was used to study the interactions between the different components and determine the appropriate mixing ratio. Considering the goals of multi-objective optimization, the optimal mix of sisal fiber-reinforced fly ash-based lateritic concrete may be produced with LA substitution of 25%, FA substitution of 10.52%, SF addition of 1%, and SP addition of 1.48%. Experimental validation was carried out with an error rate of less than 5% of the anticipated optimal data.

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing.

Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user's participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user's profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user's income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

Expectations or rational expectations? A theory of systematic goal deviation

A planner uses goals to manage a preference disagreement over effort provision with a doer. Goals set output expectations for the doer which affect her behavior due to reference-dependent, loss-averse preferences over output. We characterize the planner's optimal goal and explore when it is aspirational versus achievable. Specifically, we show that the optimal goal is achieved by the doer only if the extent of preference disagreement is relatively small. Instead, when the extent of preference disagreement is large, the doer falls short of the optimal goal. The stochasticity of output plays an important role in generating this prediction within our model.

Requirements for digitalization of optimization technologies in healthcare: sociological research

BACKGROUND: The relevance of the study is due to the insufficient study of the requirements for digitalization in healthcare.&#x0D; AIM: The aim of this research is the scientific substantiation of requirements for digitalization of optimization technologies in healthcare, and their approbation on the model of management decision-making in medical institutions.&#x0D; MATERIALS AND METHODS: An anonymous survey involved 32 teaching experts from organizational departments and the Department of Philosophy, Humanities and Psychology of Saratov State Medical University named after V.I. Razumovsky, where optimization technologies have been rapidly developed and introduced into teaching and practice. Experts were asked to use a 10-point scale to evaluate the list of requirements for digitalization of optimization technologies in healthcare, and their significance. As the data distribution did not correspond to the binomial Gauss–Newton curve, the assessment of the reliability of differences was performed using the nonparametric Mann–Whitney criterion.&#x0D; RESULTS: A list of seven requirements for the digitalization of optimization technologies in healthcare, relevant to information support, automated evaluation of the initial state, implementation of the provisions of flexible changes methodology, virtualization of the optimization goal, cyclic optimization process, unified information space, organization of professional training was designed. Since the significance of all these requirements is essential (more than 8.0 points), we can suggest that when assessing the compliance of a particular technology, the entire list should be implemented. It turned productive to use the results of designing the technology for optimization of managerial decision-making in medical institutions for approbation of reasonable requirements.&#x0D; CONCLUSION: The requirements for digitalization of optimization technology in healthcare have been substantiated. It has been proved that the technology of optimizing managerial decision-making in medical institutions is digitized. The findings of this study can be used in both assessing the degree of digitalization of optimization technologies in healthcare and the professional training of healthcare managers.

Study on Flow Field Analysis and Structure Optimization in Impeller of Single-Stage Centrifugal Compressor

Fluent's built-in Latin hypercube sampling is used to generate a sample space, a total of 32 design points, a high-precision calculation model needs to be generated by CFD, the design parameters and their value ranges are determined, the response surface is used to establish a surrogate model, and the particle swarm optimization algorithm is used to obtain the optimal design parameters of the impeller with the pressure ratio and efficiency of the single-stage centrifugal compressor as the optimization goal, so as to achieve better performance of the impeller of the single-stage centrifugal compressor.

Optimizing production of electric energy from renewable sources by differential evolution

This work deals with a general procedure for optimizing operational diagrams of electric energy production units that use alternative energy sources. The goal of optimization is to supply given load at minimal use of conventional energy sources. In order to perform the optimization, simplified mathematical models of different production units that use alternative energy sources are introduced. These units are based on solar cells, wind turbines, fuel cells and electrolysis. The optimization is performed for given weather forecasting and forecasted load diagram by the Differential Evolution, which is a stochastic search algorithm.

Transformations of sensory information in the brain suggest changing criteria for optimality.

Neurons throughout the brain modulate their firing rate lawfully in response to sensory input. Theories of neural computation posit that these modulations reflect the outcome of a constrained optimization in which neurons aim to robustly and efficiently represent sensory information. Our understanding of how this optimization varies across different areas in the brain, however, is still in its infancy. Here, we show that neural sensory responses transform along the dorsal stream of the visual system in a manner consistent with a transition from optimizing for information preservation towards optimizing for perceptual discrimination. Focusing on the representation of binocular disparities-the slight differences in the retinal images of the two eyes-we re-analyze measurements characterizing neuronal tuning curves in brain areas V1, V2, and MT (middle temporal) in the macaque monkey. We compare these to measurements of the statistics of binocular disparity typically encountered during natural behaviors using a Fisher Information framework. The differences in tuning curve characteristics across areas are consistent with a shift in optimization goals: V1 and V2 population-level responses are more consistent with maximizing the information encoded about naturally occurring binocular disparities, while MT responses shift towards maximizing the ability to support disparity discrimination. We find that a change towards tuning curves preferring larger disparities is a key driver of this shift. These results provide new insight into previously-identified differences between disparity-selective areas of cortex and suggest these differences play an important role in supporting visually-guided behavior. Our findings emphasize the need to consider not just information preservation and neural resources, but also relevance to behavior, when assessing the optimality of neural codes.

A column generation approach for the lexicographic optimization of intra-hospital transports

AbstractOver the last few years, the efficient design of processes in hospitals and medical facilities has received more and more attention, particularly when the improvement of the processes is aimed at relieving the workload of medical staff. To this end, we have developed a method to determine optimal allocations of intra-hospital transports to hospital transport employees. When optimizing transport plans in hospitals, there are various optimization goals to strive for. Therefore, we used a lexicographic approach to solve this multi-criteria optimization problem. To calculate optimal transport plans in a sufficiently short computation time, we have decomposed the problem at hand with the Dantzig–Wolfe reformulation and solved the resulting pricing subproblem with an enumerative column generation approach. To improve the efficiency of the column generation process, we have investigated and implemented different pruning methods, dominance rules and a column reuse mechanism for the online setting of the application at hand. In an extensive computational study, we first evaluated the efficiency of the different pruning methods before we compared our solution approach with the standard branch-and-bound column search approach, classical column generation methods and the solution of an integrated MIP model solved by a commercial solver. Finally, we present performance indicators of the transport optimization tool, which was developed from our method and is now productively used in a German hospital.

Stochastic bandgap optimization for multiscale elastic metamaterials with manufacturing imperfections

Bandgaps are endowed to elastic metamaterials (EMMs) attributed to the rationally designed unit cells and extensive works are devoted to bandgap enlargement for improving the applicability of EMMs in multi-disciplinary applications. Nonetheless, most existing optimization frameworks neglect manufacturing imperfections, such as microscale heterogeneity and system uncertainties, which can significantly affect bandgap behaviors. Without properly accounting for these effects, the design may fail to achieve the optimal goal, exhibiting consistently ultra-wide wave attenuation bands in practical EMMs. Herein, in this paper, a stochastic bandgap optimization framework is developed for EMMs involving manufacturing imperfections, aiming at optimizing the first two statistical moments of the normalized bandwidth (NB) simultaneously. To alleviate the large computational costs in approximating statistical moments, a surrogate model is employed to reveal the constitutive relationship between system parameters and NB for the multiscale EMM. Moreover, to solve the optimization problem effectively and efficiently, a high-order mutation strategy is proposed to develop an improved particle swarm optimization (PSO) variant, namely the adaptively high-order mutation-based PSO (AHMPSO). To demonstrate the viability and efficiency of the proposed framework, a numerical investigation is implemented on a 3D EMM, which highlights enlarged bandwidths coupling with an improvement in the robustness of optimum.