Global Performance Research Articles

A modified smell agent optimization for global optimization and industrial engineering design problems

Abstract This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active–passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration–exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10–20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously.

The Janus Face of Valuation: Global Performance Indicators as Powerful and Criticized Public Measures

The article conceptualizes global performance indicators as public measures that are powerful but also receive a wide range of criticism. Global performance indicators derive their power from combining three analytically distinct elements: (a) commensuration (comparing performances on a common metric), (b) visual simplification (presenting performances in an appealing format), and (c) serialization (framing performance as a continuous developing property). However, the very same elements are often subject to criticism. The producers of global performance indicators, therefore, defend methodologies and the validity of commensurated numbers, meet charges of visual oversimplification by professing sobriety and nuance, and balance temporal continuity and discontinuity. By conceptualizing global performance indicators as powerful and criticized public measures, the article draws attention to the Janus face of valuation, which the producers must tackle continuously. Investing considerable time, energy, and resources, these organizations are a key feature of today’s vast indicator culture.

A Comparative Study of Parameter Identification Fusion Methods With a High-Precision Supercapacitor Model for Electric Vehicles

Abstract To ensure energy storage system operates reliably for electric vehicles, it is vital to accurately identify supercapacitor model parameters in applications. In recent years, most of the algorithms focus on lithium-ion batteries, but few are reported to be used for supercapacitors. To fill this research gap, many algorithms and corresponding fusion methods for supercapacitors are designed in this study. First, seven popular intelligent optimization algorithms are selected to identify the supercapacitor model parameters, and the identification results are discussed in detail. Then, considering a single algorithm cannot guarantee convergence to all global optimal model parameters over state-of-charge (SOC) intervals, five fusion methods for supercapacitor parameter identification have been developed by combining information fusion technology. Finally, voltage errors are statistically analyzed to validate the effectiveness of the five proposed fusion methods. The results show that the five fusion methods can further enhance the global prediction performance of the supercapacitor model, particularly the reverse search-based parameter identification fusion (PIF-RS) method, which has better accuracy and reliability with respect to the maximum (Max) error, mean error, and root mean square (RMS) error decreasing by at least 10.1191%, 17.0024%, and 17.0989%, respectively.

Optimising the material emissions of single-dwelling residential buildings using the dynamic life cycle iteration protocols

While developed countries progress in environmental impact mitigation, developing economies struggle to lessen the severity of environmental impact and improve performance strategies. As of 2021, only 199 research publications were linked to African Life Cycle Assessment investigations highlighting the environmental consequences and lack of research in developing countries. In 2022 Ghana's global environmental performance was ranked 170th out of 180 countries, with a score of 27.70 out of 100, showing a ten-year trend of -6.1. This study aimed to optimise single-dwelling residential buildings' material emissions from cradle to gate using first principles through the dynamic life cycle iteration protocols. The following objectives were set: 1) to identify the primary materials in a typical Ghanaian single-family dwelling and apply first principles to model the environmental impact, and 2) to replace the materials with an environmentally friendly type through iteration optimisation. The Building Information Modelling in Revit 2021 was used to design and take off quantities of the materials. The optimisation was conducted through iterations using MS Excel. The baseline model showed that steel and aluminium products generated frontal results of 28.64 ton CO2eq/m2 and 1.96 ton CO2eq/m2, representing 92% and 6% of the total GWP100, respectively. A decrease of 39% in GWP100 emissions was made possible by employing concrete blocks instead of expandable lightweight concrete clay blocks. It is noteworthy that gypsum plasterboard had the lowest material impact across the GWP100, emitting 1895.40 kgCO2eq/m2, or 0.81 percent carbon, during the production phase. The results of this study can aid the many real estate developers and construction firms in developing nations who want to build residential homes using sustainable materials to lessen the effects of climate change caused by using building materials.

Beyond throughput: Incorporating air transport network topology in airport performance measurement

Airport ranking studies have traditionally focused on throughput criteria such as passenger and cargo volume, overlooking the importance of air transport network topology in assessing airport performance. In this work, we propose a novel approach that incorporates both traditional throughput criteria and air transport network topology criteria, such as degree centrality (direct connections) and betweenness centrality (transit connections). To ensure unbiased criteria weight estimation, we combine objective and subjective weights using Bayes’ theorem. Using our approach, we develop a Global Airport Performance Score (GAPS) to measure the performance of airports. We then apply this methodology to rank the top 50 international commercial service airports in 2019, published by Airports Council International, to validate our proposed methodology. Furthermore, we conduct a sensitivity analysis to demonstrate the robustness of our method. The results reveal valuable insights for benchmarking airports in the worldwide air transportation network. By considering both traditional throughput criteria and air transport network topology, our approach can help airport operators, policymakers, and other stakeholders make more informed decisions about airport investments and improvements.

Development of new models to predict the corrosion inhibition efficiency as functions of some molecular descriptors using statistical analysis

62 models have been proposed to predict the corrosion inhibition efficiency for a wide variety of organic and inorganic compounds using the statistical analysis. In this context, we used the experimental data of the corrosion inhibition efficiencies of 100 inhibitors (organic and inorganic) as an input data for a statistical analysis in order to determine the most adequate quantitative structure activity relationship (QSAR) models to predict the inhibition efficiency as function of some molecular descriptors. The experimental data used in this study are selected for the corrosion of steel in 0.5 M H2SO4 at 25 °C. Also, we choose the inhibition efficiencies determined by potentiodynamic polarization curves (IEp) and electrochemical impedance spectroscopy (IEEIS). Accordingly, the statistical analysis has been applied to collect the IEp and IEEIS models of corrosion inhibitors. The different models are compared to assess the performance of the best model. Global Performance Indicator (GPI) is computed to evaluate all models. The results show that the cubical model with a GPI of 1.27 is the best (M23) for IEEIS, and the linear model with a GPI of 0.64 for IEp.

Parameter identification of lithium battery pack based on novel cooperatively coevolving differential evolution algorithm

Parameter identification is of great importance for lithium battery. In this study, the parameter identification problem for a lithium battery pack is addressed, and the efficient parameter identification model and algorithm are developed by using the cooperatively coevolving theory. Firstly, the offline optimization model for battery parameter identification is established by defining the identification time-window and ultra-high dimensional optimization vector. Secondly, the variable-coupling relationship is comprehensively analysed and the developed model is proved to be a partial-separate problem. Then, by introducing the dynamic-decomposition based variable-grouping mechanism, adaptive multi-mutation mechanism and deep extended archive mechanism, a novel identification algorithm is developed to improve the performance on optimizing high-dimensional models. Finally, the developed model and algorithm are verified by a comprehensive set of case studies. Experimental results show that the aforementioned algorithmic mechanisms can significantly improve the global optimization performance. In addition, the developed algorithm can obtain accurate performance for identifying more than 8000 parameters, and can also significantly outperform the compared state-of-the-art algorithms on both accuracy and robustness.

Influence of train speed and its mitigation measures in the short- and long-term performance of a ballastless transition zone

AbstractThe ballastless track is nowadays the most popular railway system due to the required low number of maintenance operations and costs, despite the high investment. The gradual change from ballasted to ballastless tracks has been occurring in Asia, but also in Europe, increasing the number of transition zones. The transition zones are a special area of the railway networks where there is an accelerated process of track degradation, which is a major concern of the railway infrastructure managers. Thus, the accurate prediction of the short- and long-term performance of ballastless tracks in transition zones is an important topic in the current paradigm of building/rehabilitating high-speed lines. This work purposes the development of an advanced 3D model to study the global performance of a ballastless track in an embankment–tunnel transition zone considering the influence of the train speed (220, 360, 500, and 600 km/h). Moreover, a mitigation measure is also adopted to reduce the stress and displacements levels of the track in the transition. A resilient mat placed in the tunnel and embankment aims to soften the transition. The behaviour of the track with the resilient mat is evaluated considering the influence of the train speed, with special attention regarding the critical speed. The used methodology is a novel and hybrid approach that allows including short-term and long-term performance, through the development of a powerful 3D model combined with the implementation of a calibrated empirical permanent deformation model.

Evaluation of Ionospheric Delay Extraction Model Using Dual-Frequency Multisystem Observations

Ionospheric delay presents complex spatiotemporal variation characteristics and is a critical error source restricting the improvement of global navigation satellite system (GNSS) positioning, navigation, and timing performance. It is a prerequisite to constructing an accurate ionospheric delay extraction model that supports the study of the ionospheric delay characteristics. By using modernized dual-frequency and multi-system signals and inter-system bias (ISB) calibration, the performance of the ionospheric delay extraction model can be enhanced further. This study focuses on quantitatively analyzing the influence of the geometric dilution of precision (GDOP) and the redundancy of observations on the accuracy of ionospheric delay extraction, by constructing two state-of-the-art high-precision dual-frequency multi-system ionospheric delay extraction models using the tightly coupled un-difference and un-combination precise point positioning (UPPP) and carrier-to-code leveling (CCL) methods. GNSS datasets from eight international GNSS service (IGS) stations were selected to evaluate their accuracy and analyze their influencing factors. The results show that the ionospheric delay extraction model using the UPPP method is more accurate than the CCL method for both single and multiple systems. The ionospheric delay extraction model using the CCL method is insensitive to time and coordinates. The ionospheric delay extraction model using the UPPP method shows inconsistent accuracy at different times and coordinates, which reflects a correlation with the GDOP effect.

The benefits of mind wandering on a naturalistic prospective memory task

Mind wandering (MW) occurs when our attention spontaneously shifts from the task at hand to inner thoughts. MW is often future-oriented and may help people remember to carry out their planned actions (Prospective Memory, PM). Past-oriented MW might also play a critical role in boosting PM performance. Sixty participants learned 24 PM items and recalled them during an immersive virtual walk in a town. The items were divided into event-based—EB and time-based—TB. During the PM retention phase, participants were randomly assigned to a high or a low cognitive load condition, in order to manipulate MW frequency. Some PM items were encoded before this MW manipulation (pre-PM) and some during the virtual walk (post-PM). A high MW frequency was linked with better global PM performances. Spontaneous past-oriented MW predicted better pre-EB retrospective PM retrieval, while spontaneous future-oriented MW predicted better Pre-EB prospective PM retrieval. Voluntary future-oriented MW predicted better post-EB retrospective retrieval. We highlighted, for the first time, a differential impact of spontaneous MW content depending on the PM component (retrospective or prospective). Past‐oriented MW is crucial for (re)consolidating PM intentions, and episodic future thinking MW for the execution of PM intentions. We discuss the twofold functional role of MW, namely, to consolidate an already programmed intention and to plan future actions.

Safeguarding the unknown: performance measurement, academic agency and the meaning of research quality in practice

PurposeThe authors examine how performance measurement systems (PMSs) and academic agency influence the meaning of research quality in practice. The worries are that the notion of research quality is becoming too simplistically and narrowly determined by research quality's measurable proxies and that academics, especially manager-academics, do not sufficiently realise this risk. Whilst prior literature has covered the effects of performance measurement in the university sector broadly and how PMSs are mobilised locally, there is only little understanding of whether and how PMSs affect the meaning of research quality in practice.Design/methodology/approachThe study is designed as a comparative case study of two university faculties in Finland. The role of conceptual analysis plays a notable role in the study, too.FindingsThe authors find that manager-academics of the two examined faculties have rather similar conceptual understandings of research quality. However, there were differences in the degree of slippage between the “espoused-meaning” of research quality and “meaning-in-practice” of research quality. The authors traced these differences to how the local PMS and manager-academics’ agency relate to one another within the context of increasing global and national performance pressures. The authors developed a tentative framework for the various “styles of agency”. This suggests how the relationship between the local PMS and manager-academics’ exerted agency shapes the “degrees of freedom” of the meaning of research quality in practice.Originality/valueGiven that research quality lies at the heart of academic work, the authors' paper indicates that exploring the three matters – performance measurement, the agency of manager-academics and the meaning of research quality in practice – in combination is crucial for the sustainability of the academe. The authors contribute to the literature by detailing the way in which local PMS and manager-academics' agency have material impacts on what research quality means in practice. The authors conclude by highlighting the pressing need for manager-academics to exercise the agency in efforts to safeguard a broad and pluralistic understanding of research quality in practice.

Convergence analysis of ABC algorithm based on difference model

In the cross-mutation phase of the Artificial Bee Colony optimization algorithm (ABC algorithm), the value of parameter φ is very important to the stability of ABC algorithm, however, most literatures did not specify the origin of the value range of parameter φ. Through the theoretical analysis of the stability of the ABC algorithm, appropriate parameters can be selected for the algorithm under different convergence conditions and different application conditions, and then the rapid convergence of the algorithm can be achieved, so the stability analysis of the algorithm is very worth exploring. This paper firstly introduces the ABC algorithm, and then differentiates the motion trajectory equations of employed bees in the ABC algorithm to obtain a differential model; then uses the discrete system stability theory to analyze the stability of the system, and obtains the relationship between the value range of the parameters in the cross-mutation stage and the stability of the algorithm. Finally, the correctness of the conclusion will be verified by experiments using the test function. The results obtained show that ABC algorithm with φ∈(−2,0) converges faster, and has better global optimization performance than ABC algorithm with other values of φ, which are investigated in this paper.

The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results

Abstract. This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations – a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100 km atmosphere and land, a 0.125∘ river-routing model, and 14→60 km ocean and sea ice. By design, the computational cost of NARRM is ∼3× of the uniform low-resolution (LR) model at 100 km but only ∼ 10 %–20 % of a globally uniform high-resolution model at 25 km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air–sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land–atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification.

The features of the kernel extreme learning machine-efficient processing, improved performance, and less human parameter setting-have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space-time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model's generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm's current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

Influence of applied pressure on the tribological behaviors of a wet multi-disc clutch during the running-in process

PurposeThis paper aims to explore the influence of applied pressure on the tribological properties of the friction component in a wet multi-disc clutch during the running-in process.Design/methodology/approachThe running-in evolutionary was explored in terms of global friction performance. The variation of friction torque and mean COF of the initial 300 engagement cycles was obtained by full-scale tests. Finally, an optical microscope was used to detect the wear characteristics of friction surfaces.FindingsThe applied pressure showed a significant influence on the tribological behaviors of wet clutches during the running-in process. The mean COF decreased and then increases with the increase of the applied pressure. A higher applied pressure contributed to more asperity summits being sheared, thus resulting in a smoother surface. Considering a suitable wore performance, properly applied pressure is necessary.Originality/valueThe results provide theoretical guidance for selecting the optimal applied pressure in the running-in of wet clutches.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2022-0256/

24 h Rest/Activity Rhythms in Older Adults with Memory Impairment: Associations with Cognitive Performance and Depressive Symptomatology.

Little is known about links of circadian rhythm alterations with neuropsychiatric symptoms and cognition in memory impaired older adults. Associations of actigraphic rest/activity rhythms (RAR) with depressive symptoms and cognition are examined using function-on-scalar regression (FOSR). Forty-four older adults with memory impairment (mean: 76.84 ± 8.15 years; 40.9% female) completed 6.37 ± 0.93 days of actigraphy, the Beck depression inventory-II (BDI-II), mini-mental state examination (MMSE) and consortium to establish a registry for Alzheimer's disease (CERAD) delayed word recall. FOSR models with BDI-II, MMSE, or CERAD as individual predictors adjusted for demographics (Models A1-A3) and all three predictors and demographics (Model B). In Model B, higher BDI-II scores are associated with greater activity from 12:00-11:50 a.m., 2:10-5:50 p.m., 8:40-9:40 p.m., 11:20-12:00 a.m., higher CERAD scores with greater activity from 9:20-10:00 p.m., and higher MMSE scores with greater activity from 5:50-10:50 a.m. and 12:40-5:00 p.m. Greater depressive symptomatology is associated with greater activity in midafternoon, evening, and overnight into midday; better delayed recall with greater late evening activity; and higher global cognitive performance with greater morning and afternoon activity (Model B). Time-of-day specific RAR alterations may affect mood and cognitive performance in this population.

Association of Performance on Olfactory and Cognitive Screening Tests With Conversion to Dementia in a Biracial Cohort of Older Adults.

Odor identification deficits are associated with transition to dementia, whereas intact odor identification and global cognition test performance may identify lack of transition. The purpose of this study was to examine intact odor identification and global cognition as prognostic indicators of lack of transition to dementia in a biracial (Black and White) cohort. In a community-dwelling sample of older adults from the Health, Aging, and Body Composition study, odor identification was measured using the Brief Smell Identification Test (BSIT), and global cognition was measured using the Teng Modified Mini-Mental State Examination (3MS). Survival analyses for dementia transition over 4 and 8 years of follow-up used Cox proportional hazards models. A total of 2,240 participants had an average age of 75.5 years (SD 2.8). Approximately 52.7% were female individuals. Approximately 36.7% were Black and 63.3% were White individuals. Impaired odor identification (hazard ratio [HR] 2.29, 95% CI 1.79-2.94, p < 0.001) and global cognition (HR 3.31, 95% CI 2.26-4.84, p < 0.001) were each independently associated with transition to dementia (n = 281). Odor identification remained robustly associated with transition to dementia for Black (HR 2.02, 95% CI 1.36-3.00, p < 0.001, n = 821) and White participants (HR 2.45, 95% CI 1.77-3.38, p < 0.001, n = 1,419), whereas global cognition was associated with transition among Black participants only (HR 5.06, 95% CI 3.18-8.07, p < 0.001). ApoE genotype was consistently associated with transition among White participants only (HR 1.75, 95% CI 1.20-2.54, p < 0.01). Among participants with intact performance on both odor identification (BSIT ≥9/12 correct) and global cognition (3MS ≥ 78/100 correct), 8.8% transitioned to dementia over 8 years. Intact performance on both measures had high positive predictive value for identifying individuals who did not transition to dementia over 4 years (0.98 for ages 70-75 years with only 2.3% transitioning, 0.94 for ages 76-82 years with only 5.8% transitioning). Odor identification testing paired with a global cognitive screening test identified individuals at low risk of transition to dementia in a biracial community cohort with a pronounced effect in the eighth decade of life. Identification of such individuals can reduce the need for extensive investigation to establish a diagnosis. Odor identification deficits showed utility in both Black and White participants, unlike the race-dependent utility of a global cognitive test and ApoE genotype.

Nonexcessive-ΔV and Low Complexity Model Predictive Control Based on Finite-State Machine for Three-Level Three-Phase Inverters

For three-level three-phase inverters, a novel finite-state machine based model predictive control (FSM-MPC) is proposed. The state transition diagram of FSM is regarded as the operation guideline to avoid excessive voltage jumps (Δ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> ). No more than five voltage vectors (VVs) are selected as FSM-MPC's candidates VVs, based on the VV reference and the previous optimal VV. And the cost function is simplified in terms of time duration. Thus, evaluating a candidate VV requires a very low computation cost. Without losing control performance, the proposed FSM-MPC's execution time is decreased to 50% of the MPC algorithm, which enumerates all basic VVs to get global optimal VV and performance. Compared to the existing simplified algorithms, the proposed FSM-MPC makes current harmonics lower, and its average switching frequency is 33% less, which means much less switching loss. Furthermore, the proposed algorithm is robust when the electric-circuit parameters are mismatched in the control system. Experimental results are provided to validate the advantages of the proposed algorithm. <fn fn-type="other" id="fn3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This work was supported in part by National Natural Science Foundation of China under Grant 52177204, and in part by Natural Science Foundation of Hunan Province under Grant 2021JJ30875. (Corresponding author: Jian Yang) H. Zhou, J. Yang, D. Song and M. Dong are with the school of Automation, Central South University, Hunan 410083, People's Re-public of China (e-mail: ; ; ; ). L. Huang and X. Chen are in Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, People's Republic of China (e-mail: ; ). </fn>

A novel adaptive algorithm of particle swarm optimization based on the human social learning intelligence

A novel adaptive algorithm of particle swarm optimization is proposed in this paper, where the learning strategy is designed by simulating features of human social learning intelligence. By the principles of this strategy, the swarm is first adaptively grouped into a number of subswarms based on the fitness levels of particles, as well as no need of fixing the population sizes as done in the existing algorithms. Then, the particles in each subswarm constitute three different types classified by their evolutionary levels, which adopt different learning modes defined by distinct adaptive inertia weighting rules and dynamical learning mechanisms. Consequently, all the particles play their respective roles in promoting the global optimization performance of our algorithm, just like all the individuals in society. Numerical tests of our algorithm are conducted by solution of the standard benchmark test problems from the CEC 2014 and CEC 2017 test suites. The test results show the advantages of the proposed learning strategy in this paper, and demonstrate that the overall performance of our algorithm outperforms both of the other compared PSO variants and the other state-of-the-art evolutionary algorithms, in terms of global search ability, solution accuracy and convergence speed. Statistical tests also validate the promising performance of our algorithm.

The Role of an Integrated Quality Management System in Improving Performance in the Port Organization

Abstract The scientific paper aims to present the performance evaluation methods in ports and port organizations and the contributions that an integrated quality management system can bring. Water transport is the most reliable mode of transport, both in terms of transport capacity and in terms of consumption and environmental aspects, compared to other modes of transport. But it cannot function at an optimal level without complex and permanent on-shore management. The port organizations have an important contribution in the global performance of the port and thus in the economy of a country that is open to the sea. By approaching an integrated quality management system, port organizations can achieve the expected performance in a planned and systemic way in terms of quality, environment, logistics supply chain security, etc. The paper analyzes the performance assess in ports and port organizations, the most common applied regulations and management systems in port organizations and the advantages an integrated quality management system brings in increasing organizational performance by preventing losses and improving processes using known methodologies globally promoted such as Lean. It also highlights the relationship of mutual dependence that the port performance and the port organizations performance have on each other.