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Future transportation will replace the current mechanical combustible engines with chargeable electric vehicles (EVs). The DC-Dc boost converter is an essential part of such systems that not only helps to generate the required level but also helps to regulate the load voltage. In conventional boost converter topologies, their gain, switching losses, and high voltage stress across switches are the main reason for limiting the output power. Coupled inductor-based isolated converters increase the system weight and cost. Therefore, in this paper, a transformerless adjustable gain and non-isolated DC-DC boost converter with multi-port flexible power supply, reduced switch voltage stress, and considerable good range efficiency is proposed. The proposed converter is tested with the 100 W resistive load to test the desired characteristics. [ABSTRACT FROM AUTHOR]

In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions. [ABSTRACT FROM AUTHOR]

Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy. [ABSTRACT FROM AUTHOR]

With the deployment of renewable energy generation, home energy storage systems (HESSs), and plug-in electric vehicles (PEVs), home energy management systems (HEMSs) are critical for end users to improve the increasingly complicated energy production and consumption in the home. However, few of the previous works study the impact of different models of battery degradation cost in the optimization strategy of a comfort-based HEMS framework. In this paper, a novel scheduling algorithm based on a mixed-integer programming (MIP) model is proposed for the HEMS. Total cost minimization, peak load shifting, and residents' thermal comfort satisfaction are combined and considered in the optimal scheduling algorithm. The impact of battery degradation costs on the charging and discharging strategy of HESS and PEV is also compared and discussed in this case study. This case study shows that the proposed optimal algorithm of HEMS not only flattens the peak load and satisfies the thermal comfort of residents but also has better flexibility and economic advantages, reducing the electricity cost by 30.84% and total cost by 24.16%. The sensitivity analysis of the parameters for the charging and discharging strategy also guarantees the lowest cost and prolongs the service life of the battery. [ABSTRACT FROM AUTHOR]

Summary: Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium‐ion battery (LIB) and a supercapacitor (SC)‐based HESS (LIB‐SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal‐sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system‐level optimizations, include component sizing (CS) and power‐energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB‐SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB‐SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB‐SC HESS are provided to inspire researchers for further studies. Highlights: Lithium‐ion battery (LIB) and supercapacitor (SC)‐based hybrid energy storage system (LIB‐SC HESS) suitable for EV applications is analyzed comprehensively.LIB‐SC HESS configurations and suitable power electronics converter topologies with their comparison are provided.System‐level optimization of LIB‐SC HESS and generalized steps involved in implementing the optimization algorithm for component sizing (CS) and power‐energy management strategy (PEMS) are discussed.A rigorous study on CS and PEMS is presented to develop efficient LIB‐SC HESS.Current challenges and future recommendations for the development of LIB‐SC HESS for EV applications are provided. [ABSTRACT FROM AUTHOR]

To achieve a more sustainable supply of electricity and reduce dependency on fuels, the application of renewable energy sources-based distribution systems (DS) is stimulating. However, the intermittent nature of renewable sources reduces the overall inertia of the power system, which in turn seriously affects the frequency stability of the power system. A virtual synchronous generator can provide inertial response support to a DS. However, existing active power controllers of VSG are not optimized to react to the variation of frequency changes in the power system. Hence this paper introduces a new controller by incorporating GA-ANFIS in the active power controller to improve the performance of the VSG. The advantage of the proposed ANFIS-based controller is its ability to optimize the membership function in order to provide a better range and accuracy for the VSG responses. Rate of change of frequency (ROCOF) and change in frequency are used as the inputs of the proposed controller to control the values of two swing equation parameters, inertia constant (J) and damping constant (D). Two objective functions are used to optimize the membership function in the ANFIS. Transient simulation is carried out in PSCAD/EMTDC to validate the performance of the controller. For all the scenarios, VSG with GA-ANFIS (VOFIS) managed to maintain the DS frequency within the safe operating limit. A comparison between three other controllers proved that the proposed VSG controller is better than the other controller, with a transient response of 22% faster compared to the other controllers. [ABSTRACT FROM AUTHOR]

Research into modern transportation systems is currently in progress in order to fully replace the traditional inter-combustible engine with a noiseless, fast, energy-efficient, and environmentally friendly electric vehicle. Electric vehicles depend on an electric motor and require highly efficient converter drive circuits. Among these converters, DC-DC boost converters play a major role in charging not only the battery banks but also in providing the DC-link excitation voltage in transformerless applications. However, the development of these converters, which have higher voltage and current gain with minimum components, minimum voltage, and current stress, is quite challenging. Therefore, this research work aims to address these issues and also to improve overall system performance. These aims are achieved by developing a series LC-based single-stage boost converter, and extending its gain through a multi-stage boost converter using switch capacitor phenomena. This article also presents a complete operating model in continuous conduction mode. The proposed converter is tested under various testing conditions, such as output loading, input voltage levels, and duty cycle ratio for a 50 W resistive load. The results are compared with existing models. The proposed converter is stated to have achieved the highest efficiency, i.e., 96.5 % , along with extendable voltage gain with reduced voltage and current stresses, which is a major contribution to this research field. [ABSTRACT FROM AUTHOR]

Nowadays, distributed generation (DG) has become an indispensable part for meeting the growing power demand in electrical power generation and distribution. However, one of the drawbacks of DG is unintentional islanding phenomena, which has become a safety issue for both human and equipment connected to the system. To prevent this hazardous condition, according to IEEE 1547 standards, this islanding condition must be detected within 2 s. This paper proposed an approach to develop a hybrid islanding detection method (IDM) to prevent the damages caused by this islanding condition. The proposed hybrid IDM is a combination of three different IDMs, two passive and one active; that is, rate of change of active power (ROCOAP) and rate of change of reactive power (ROCORP) are passive IDMs where load connecting strategy (LCS) is active. To differentiate islanding conditions with similar occurrences, different case studies are taken into account with photovoltaic (PV) and synchronous generator (SG) working as distributed generators on PSCAD/EMTDC platform. The simulation results confirm that proposed IDM is more favorable compared to other IDMs due to simplicity and fast islanding detection time when its performance is tested on the 11‐kV Malaysian distribution system for various cases. [ABSTRACT FROM AUTHOR]

In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter. [ABSTRACT FROM AUTHOR]

This paper proposed a fractional order PID sliding mode control (FOSMC-PID) for speed regulation of permanent magnet synchronous motor (PMSM). Fractional calculus has been incorporated in sliding mode controller (SMC) design to enhance chattering suppression ability. However, the design of fractional sliding surface is crucial to ensure that speed tracking accuracy is not jeopardized. The proposed controller is designed with a fractional order PID sliding surface, which balances the characteristics of sliding surface with PI or PD structure in terms of robustness and dynamic performance of the controller. By simulation, speed tracking is proven to be faster and more robust with the proposed controller compared to SMC with integer order. Both integration and derivative terms in the surface design outperform FOSMC-PI and FOSMC-PD in terms of disturbance rejection and chattering. Experimental validation proves the advantage of the proposed controller in terms of robustness. [ABSTRACT FROM AUTHOR]

The exponential increase in the frequency and intensity of high impact low probability weather‐related events have pivoted the paradigm of research pertaining to power systems towards resilience. Power system is considered as a critical infrastructure, directly linked to the nation's economy, security and health. Therefore, recent researchers have proposed several techniques to enhance the resilience of power systems. In those techniques, critical loads have been considered independent in nature and different metrics have been proposed to evaluate the resilience of the network. To enhance the resiliency, this paper incorporated distributed generators in the distribution network and critical loads are modelled interdependently. Furthermore, a novel resilience metric is proposed in this paper to evaluate the resilience of a distribution system. The proposed model is formulated as a mixed integer second‐order cone programming problem and the efficacy of the proposed model is evaluated on IEEE 33‐ and 69‐bus systems. The competence of the proposed resilience metric is evaluated after comparison with existing resilience metrics. [ABSTRACT FROM AUTHOR]

Virtual synchronous generator (VSG) is an important concept toward frequency stabilisation of the modern power system. The penetration of power electronic‐based power generation in power grid reduces the total inertia, and thus increases the risk of frequency instability when disturbance occurs in the grid. VSG produces virtual inertia by injecting appropriate active power value to the grid when needed. This virtual inertia can stabilise the grid frequency in case of a power imbalance between generation and loads or any disturbances that affected frequency stability. Its intensive research can see the importance of VSG in inertia control and various intelligent controller techniques. Owing to the importance of VSG in the modern power grid, this study provides a comprehensive review on the control and coordination of VSG toward grid stabilisation in terms of frequency, voltage and oscillation damping during inertia response. A review on the type of energy storage system used for VSG and their benefits is also presented. Finally, perspective on the technical challenges and potential future research related to VSG is also discussed in this study. [ABSTRACT FROM AUTHOR]

An electrical power system is considered as a critical infrastructure (CI), the epicenter of a nation's economy, security, and health. It is interlinked with other CIs such as gas and water supplies and transportation and communication systems. A failure in the power system will immensely affect the functionality of these CIs. Therefore, enhancing power system resilience is crucially needed to ensure continuous operation of these CIs. One of the possible approaches to improve the resilience in a power system is by integrating microgrids in the power system. Microgrids have proven to have self-healing and resilient capabilities in such extreme events which inflict damage out of the conventional scope of failures. Operational flexibility and controllability make microgrids a viable solution for resilience enhancement. This paper reviews the concept of resilience in power systems and the functions of microgrids in enhancement of resilience. The most current studies in improving power system resilience through microgrids are reviewed by highlighting their advantages and limitations. [ABSTRACT FROM AUTHOR]

This study presents selective harmonic elimination pulse width modulation technique-based hybrid asynchronous PSO-Newton-Raphson (APSO-NR) algorithm for the elimination of undesired harmonics in cascaded H-bridge multilevel inverter. The proposed algorithm is applicable to all levels of MLI having equal and non-equal DC sources. In the proposed method, ring topology-based APSO algorithm is hybrid with NR method. APSO worked as a global search technique and NR is used for the refinement of best solutions. APSO-NR is applied to the seven-level inverter to eliminate fifth and seventh harmonics. In simulations, the performance of the proposed algorithm is compared with genetic algorithm, bee algorithm and particle swarm optimisation. The results proved that the proposed algorithm is efficient, and gives more precise firing angles in less number of iterations with high capability of tackling local optima. For the 48% of modulation index range, APSO-NR minimised the fitness function value lower than (10-25). The proposed algorithm is validated through the experimental implementation of the three-phase seven-level inverter. [ABSTRACT FROM AUTHOR]

A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs. [ABSTRACT FROM AUTHOR]

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model. [ABSTRACT FROM AUTHOR]

Abstract—Model predictive control has emerged as a powerful control tool in the field of power converter and drive's system. In this article, a weighting factor optimization for reducing the torque ripple of induction machine fed by an indirect matrix converter is introduced and presented. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponding to minimum torque ripple. However, model predictive torque and flux control of the induction machine with conventionally selected weighting factor is being investigated in this article and is compared with the proposed optimum weighting factor based model predictive control algorithm to reduce the torque ripples. The proposed model predictive control scheme utilizes the discrete phenomena of power converter and predicts the future nature of the system variables. For the next sampling period, model predictive method selects the optimized switching state that minimizes a cost function based on optimized weighting factor to actuate the power converter. The introduced weighting factor optimization method in model predictive control algorithm is validated through simulations and shows potential control, tracking of variables with their respective references and consequently reduces the torque ripples corresponding to conventional weighting factor based predictive control method. [ABSTRACT FROM PUBLISHER]

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalizedmodel. In this study, feature selection and classifier parameters estimation based on particle swarmoptimization (PSO) are proposed as a framework for peak detection on EEGsignals in time domain analysis.Two versions of PSOare used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO).The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments.The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. [ABSTRACT FROM AUTHOR]

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. [ABSTRACT FROM AUTHOR]

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarmperformance is evaluated. This algorithm is also known as synchronous PSO (S-PSO).The strength of this updatemethod is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problemare used to study the performance of SA-PSO,which is comparedwith the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well. [ABSTRACT FROM AUTHOR]

In the automatic detection of Cryptosporidium and Giardia (oo)cysts in water samples, low contrast and noise in the microscopic images can adversely affect the accuracy of the segmentation results. An improved partial differential equation (PDE) filtering that achieves a better trade-off between noise removal and edge preservation is introduced where the compass operator is utilized to attenuate noise while retaining edge information at the cytoplasm wall and around the nuclei of the (oo)cysts. Then the anatomically important information is separated from the unwanted background noise using the Otsu method to improve the detection accuracy. Once the (oo)cysts are located, a simple technique to classify the two types of protozoans using area, roundness metric and eccentricity is implemented. Finally, the number of nuclei in the cytoplasm of each (oo)cyst is counted to check the viability of individual parasite. The proposed system is tested on 40 microscopic images obtained from treated water samples, and it gives excellent detection and viability rates of 97% and 98%, respectively. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are known as highly efficient motors and are slowly replacing induction motors in diverse industries. PMSM systems are nonlinear and consist of time-varying parameters with high-order complex dynamics. High performance applications of PMSMs require their speed controllers to provide a fast response, precise tracking, small overshoot and strong disturbance rejection ability. Sliding mode control (SMC) is well known as a robust control method for systems with parameter variations and external disturbances. This paper investigates the current status of implementation of sliding mode control speed control of PMSMs. Our aim is to highlight various designs of sliding surface and composite controller designs with SMC implementation, which purpose is to improve controller's robustness and/or to reduce SMC chattering. SMC enhancement using fractional order sliding surface design is elaborated and verified by simulation results presented. Remarkable features as well as disadvantages of previous works are summarized. Ideas on possible future works are also discussed, which emphasize on current gaps in this area of research. [ABSTRACT FROM AUTHOR]

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms.

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well.