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This paper addresses the joint optimization model of production scheduling, work-in-process (WIP) inventory control and group preventive maintenance (PM) planning in a multi-machine system with multi-components. The objective is to obtain optimum production sequence, PM intervals and grouping of components, which minimize the total expected cost per unit time of the system. A new meta-heuristic named Jaya algorithm and two popular algorithms viz. simulated annealing (SA) and particle swarm optimization (PSO) are applied to optimize the objective function. Initially, the optimum group of components is obtained based on the integer multiples of individual PM intervals. Secondly, the job permutation sequence incorporating group PM intervals is identified with the largest order value (LOV) rule. The shift in optimum PM intervals is realized with an advanced-postpone balancing approach. Computational results reveal that the proposed integrated model along with group PM yields up to 25% cost reductions when compared to the integrated model with individual maintenance as well as 37% savings while no integration is performed. Furthermore, the performance of algorithms is evaluated with large-sized problems. The obtained results show that Jaya and SA yielded comparable results, however, PSO is least productive. Thus, the proposed approach yields better economic performance and brings more improvised solutions as compared to the conventional methods of integrated scheduling and maintenance optimization problems. [ABSTRACT FROM AUTHOR]

Utility Grid outages may affect the industrial power system load-generation balance and cause rapid frequency decays particularly due to low inertia and small spinning reserve. Frequency stability is an essential perspective for the invulnerable operation of islanded industrial power systems with in-plant generation. Multi-machine system frequency response (MMSFR) model with the dynamic of governors, overall inertia of the system (generators and loads), and load damping is considered to find the approximate value of load shedding (LS) amount. The frequency variation of the islanded system is evaluated for various operating scenarios with a calculated approximate LS amount to determine the optimum amount of LS. In this paper, an artificial neural network (ANN)-based optimum and adaptive LS technique have been presented. The total in-plant generation, spinning reserve, total power import, total demand, and frequency decay rate have been selected as the input neurons of the ANN. The regrouping particle swarm optimization (RegPSO) algorithm has been adopted to obtain more accurate and faster training of the ANN. The oil refinery power distribution system with in-plant generation is considered to evaluate the performance of the proposed RegPSO-ANN load shedding algorithm. The performance of the proposed scheme is evaluated in comparison with the under-frequency relay-based conventional load shedding scheme and Levenberg–Marquardt back-propagation ANN-based LS scheme. [ABSTRACT FROM AUTHOR]

Low-frequency oscillations (LFO) are a significant problem for multi-machine electrical power system (EPS). These oscillations are undesirable as they reduce the power transfer capability of the transmission line and thus directly influence competitive electrical markets. The power system stabilizers (PSS) play a vital role in damping low-frequency oscillations to improve the dynamic stability of the power system. However, these controllers must be tuned in a coordinated and robust manner for effective performance. PSS tuning is characterized by a complex optimization problem that may involve hundreds of variables. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients of the closed-loop power system considering multiple operating points. In this context, this article employs and compares three metaheuristics with quantum characteristics, namely quantum particle swarm optimization (QPSO), quantum flower pollination algorithm (QFPA) and quantum gray wolf optimizer (QGWO). The aforementioned metaheuristics are used in PSS tuning in four test systems with 5 generators–7 buses, 10 generators–39 buses, 50 generators–616 buses and 170 generators–3584 buses. The results obtained by the quantum algorithms are compared through statistical indices and boxplot. The optimization results show that the QFPA and QGWO provide better system damping than the QPSO for the large electrical system. [ABSTRACT FROM AUTHOR]

With the advent of the era of information explosion, multimedia big data with images as the main carrier has increased dramatically and has penetrated into various fields of people's lives. As the basis of image recognition, classification has become an important tool for analyzing and understanding image information. Therefore, how to quickly and efficiently image classification has become a challenging research hotspot. In order to solve the limitation of traditional machine learning algorithm in classification performance, an image classification method based on chaotic optimization machine learning algorithm is proposed. This method combines the support vector machine in the machine learning algorithm with the chaotic time series to construct the classification prediction model. The particle swarm optimization algorithm is used to realize the optimal parameter search of the support vector machine to improve the performance of the prediction model. The test results show that compared with other multi-machine learning algorithm predictions, the proposed method effectively reduces the dependence of classification results on sample types and features, and has a higher and robust classification effect. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid approach to increase the performance reliability of power distribution networks using phasor measurement units (PMU) is presented. Electricity distribution networks are very important as a vital part of electricity transmission systems in providing energy to users. Considering the ever-increasing complexity and the huge amount of existing demand, maintaining the optimal performance and reliability of these networks is vital. One of the main challenges in this industry is identifying and finding problems. In this regard, improvements in phasor measurement technology using phasor measurement units (PMU) allow engineers in this industry to more accurately evaluate data, diagnose and locate network faults. In this research, one of the non-linear methods for finding ground faults in power distribution networks using voltage phasor measurement in several network stations using phasor measurement units (D-PMU) has been demonstrated. In the first approach, genetic optimization algorithms and bullet optimization algorithm (PSO) have been used for nonlinear modeling of fault position estimation based on different types of 1-phase, 2-phase and 3-phase faults. The second method uses fuzzy network training to provide details about phasor voltages and fault types. By simulating a 9-station system using MATLAB software, the usefulness of the proposed methods has been shown. In modeling, 1-phase, 2-phase and 3-phase faults along with different line lengths and line characteristics at different stations have been investigated. Also, the findings are presented and the location of the defect is identified immediately. [ABSTRACT FROM AUTHOR]

This study concentrates on the implementation of Marine Predator Algorithm (MPA) scheme for tuning of a power system stabilizer's (PSS's) parameters to damp the low-frequency oscillations in a power system. To this, the single machine infinite bus system (SMIB), the Western System Coordinating Council (WSCC) and the New England 10 machine 39-bus power system are utilized for testing and comparing different metaheuristic algorithms using different fitness functions. Optimal PSS parameters of SMIB test system are validated using CU-SLRT Std, a real-time digital simulator. The comparative studies demonstrate that the MPA optimized PSS yields improvements of up to 98.62% in the Particle Swarm Optimization (PSO) at 69.42%, Whale Optimization Algorithm (WOA) at 71.79%, Flower Pollination Algorithm (FPA) at 72.39%, African vulture optimization algorithm (AVOA) at 78.04%, Wild Horse Optimization (WHO) algorithm at 68.57% under various operating scenarios. The superiority of the MPA optimized PSS has been validated using Hardware-in-the-loop implementation for the SMIB test system. [ABSTRACT FROM AUTHOR]

The multi-machine scheduling problems with job-dependent and machine-dependent learning effects are proposed in this paper. Since it is almost impossible to obtain the analytic results for this complicated multi-machine scheduling problems with learning effects, four heuristic algorithms are used to solve this newly proposed model, where the variants of well-known genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) and particle swarm optimization (PSO) are coded in the commercial software MATLAB. The objective is to minimize the makespan of this new model. For this kind of scheduling problem, the numerical experiments show that the GA and SA outperform ACO and PSO. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization procedure based on eigenvalues to carry out the stabilization function of the Gate-Controlled Series Capacitor (GCSC) in a power system. It is aimed to provide a reliable damping framework by means of a GCSC based multi-objective damping controller. The proposed method employs Particle Swarm Optimization (PSO) to search for optimal parameter settings of a widely used multi-objective lead-lag damping controller. The eigenvalue analysis is considered as the cornerstone of the performed studies in order to investigate the multi-objective methodology in which the unstable or lightly damped modes are scheduled to effectively shift to some prescribed stability zones in the s-plane. The effectiveness of the suggested approach in damping local and interarea oscillations modes in a multi-machine power system, over a wide range of loading conditions, is confirmed through eigenvalue analysis and time simulation. [ABSTRACT FROM AUTHOR]

The paper demonstrates a comprehensive performance assessment of the two metaheuristic swarm-based optimization algorithms namely PSO (Particle swarm optimization), BFOA (Bacterial foraging optimization algorithm), and the hybrid PSO-BFOA optimizer for the alleviation and control of the power oscillations in a two-area four generator system integrated with a large-scale PV-farm. After sunset, the PV-plant operates as VSC (Voltage Source Converter)-STATCOM (Static synchronous compensator) using its overall inverting capabilities for the power system stability improvement. While in the daytime during the faults, the PV-farm immediately stops the active power production and behaves as PV-STATCOM until the normal operating conditions are resumed. The modified version of Kundur's two-area system comprising of a large-scale PV-farm is simulated with MATLAB software. An innovative control strategy employing the two PI controllers distinctly controls the DC-AC currents of the PV-STATCOM. The series compensation is set to an optimal value of 85% and subjected to a 3-φ fault. Zero mechanical dampings, along with extra disturbances of 20% variation in reference voltage and electromagnetic torque are introduced to flaunt the worst damping scenarios. The simulation outcomes and time-domain analysis for various test conditions: without a controller, with PSO-based PV-STATCOM, with BFOA-based PV-STATCOM, and with the Hybrid PSO-BFOA-based PV-STATCOM, reveal that all the system modes are stabilized with PSO application. The stability of modes is progressively improved with BFO control, eventually, the modes are optimally stabilized by deploying the hybrid PSO-BFO algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes the application of a Basic Variable Neighborhood Search algorithm in the coordinated and simultaneous tuning of the parameters of damping controllers known as power system stabilizer and thyristor-controlled series capacitor-power oscillation damping. The controllers are inserted into the multi-machine power system New England (10 generators, 39 buses and 46 transmission lines) in order to guarantee its small-signal stability. A current injection model for the thyristor-controlled series capacitor is presented and incorporated into the current sensitivity model, which is used to represent the electric power system and its components. The performance of the method proposed in this work is compared to three other methods found in the literature: local search, iterated local search and particle swarm optimization. The results show that, of the techniques analyzed, the Basic Variable Neighborhood Search is the most efficient for this type of problem, presenting high convergence rates and the shortest processing times with robust solutions considering different scenarios with load variations. [ABSTRACT FROM AUTHOR]

The Wind Farms (WFs) are located far away from the load centres, with the resultant equivalent reactance being more than near the load. It results in a weak grid due to high power transferred in the transmission lines that affect the low frequency of oscillations, and the system exists with undamped functions. The transmission line has to be compensated to transmit wind power effectively. The Static Synchronous Series Compensator (SSSC) connects WF and the load centre. This work demonstrates a Power Oscillation Damper (POD) to WFs and SSSC for transmission lines and their combination. The eigenvalue of the damping ratio and frequency of oscillations are considered objective functions for particle swarm optimization and formulated based on stability to optimize the controller parameter for improving stability. The coordination between the POD and SSSC has been conducted using a Fuzzy Logic Controller (FLC) to further enhance system performance. The small signal and transient stability examinations use eigenvalue analysis and time-domain simulation. It is analysed by varying the length (km) of the transmission line between the WF and the load centre, along with variation in wind penetration. The study observed that small signal stability damped the oscillatory modes most effectively. It is also revealed that the controller's combination with FLC intensifies the capability under extreme disturbances and detects an improvement in the system's transient stability. The proposed approaches' effectiveness is evaluated on an IEEE-11 bus two-area test system with optimized controller gain parameters and controller coordinated FLC through a Real-Time Digital Simulator. [ABSTRACT FROM AUTHOR]

In order to realize the coordinated design of multiple flexible AC transmission systems (FACTS) devices in the wind–PV–thermal-bundled (WPTB) power transmission system to suppress the low-frequency oscillation (LFO) of the system, in this paper, the static synchronous compensator (STATCOM) and the static synchronous series compensator (SSSC) with additional power oscillation damping controller (PODC) are designed based on the tie line transmission power signal to enhance the improvement effect of FACTS devices on system stability. The objective function is constructed by considering the transient voltage stability of the WPTB power transmission system, the real part of the eigenvalues and damping level of the LFO modes. And the coordinated and optimized design of power system stabilizer (PSS), SSSC-PODC and STATCOM-PODC is performed by the hybrid particle swarm optimization algorithm and genetic algorithm (PSO-GA) and pattern analysis theory of LFO. Finally, it is verified that the design can give full play to the effect of different controllers and significantly improve the damping ratio of the oscillation mode between regions through the simulation under the output variation of the new energy unit and the transmission power variation of the liaison line. When the fault occurs, the amplitude of oscillation and the time required to recover stability of the system decrease obviously, which enhances the dynamic stability of the system. [ABSTRACT FROM AUTHOR]

novel technique for the optimal tuning of power system stabilizer (PSS) was proposed, by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC). Firstly, a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC). It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. Secondly, the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima. The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSOC was compared to the MPSO, PSO and GA through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs. The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA, PSO and MPSO. [ABSTRACT FROM AUTHOR]

In this work, we intend to propose multiple hybrid algorithms with the idea of giving a choice to the particles of a swarm to update their position for the next generation. To implement this concept, Cuckoo Search Algorithm (CSA), Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Whale Optimization Algorithm (WOA) have been utilized. Exhaustive possible combinations of these algorithms are developed and benchmarked against the base algorithms. These hybrid algorithms have been validated on twenty-four well-known unimodal and multimodal benchmarks functions, and detailed analysis with varying dimensions and population size is discussed for the same. Further, the efficacy of these algorithms has been tested on short-term electricity load and price forecasting applications. For this purpose, the algorithms have been combined with Artificial Neural Networks (ANNs) to evaluate their performance on the ISO New Pool England dataset. The results demonstrate that hybrid optimization algorithms perform superior to their base algorithms in most test cases. Furthermore, the results show that the performance of CSA-GWO is significantly better than other algorithms. [ABSTRACT FROM AUTHOR]

Bacterial foraging algorithm (BFA) is a novel nature-inspired algorithm that mimics the social foraging behavior of E. coli. Bacteria. However, it gets stuck in the local optima trap and yields poor convergence in complex landscapes. To improve the exploration-exploitation balance and achieve the global optima quickly, this paper proposes a novel hybrid called the Bacterial foraging algorithm-firefly algorithm (BFA-FA). In this work, two strategies namely adaptive strategy and leadership strategy are applied on conventional BFA. The performance is examined on standard, non-linear and CEC_2017 benchmark functions over several evaluation parameters. The results on benchmark functions show that BFA-FA provides accurate solutions, avoids local optima, works well on multimodal and multidimensional landscapes, and converges faster. It also shows the statistically significant difference among other algorithms. The proposed algorithm is applied on two classical engineering problems to validate its robustness and applicability. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic stability of the power systems is improved through the rotor side convertor voltage control of a number of doubly fed induction generators (DFIGs). The multi-input backstepping method is used to design the control laws. Using the particle swarm optimization algorithm, the proposed control parameters are optimized to achieve a better performance. This optimal control law leads to a significantly improved performance in comparison with linear control methods such as state feedback control which implement optimized pole placement by linear matrix inequality design. The offered method results in a faster convergence rate and also robustness against changes in the operating points. The performance of the presented control scheme is validated in a standard 9-bus IEEE power system, with a DFIG located in bus number 9. [ABSTRACT FROM AUTHOR]

Within neural networks, it is considered a difficult task to find optimal values for the number of hidden neurons and connection weights simultaneously. This is because altering the hidden neurons significantly affects a neural network's structure and increases the difficulty of the training process which needs special considerations. Particle swarm optimization (PSO) is one of the most important metaheuristic algorithms due to its convergence speed and its simplicity of implementation. Multi-verse optimization (MVO) based on Lévy flight is a recent and fast algorithm and can avoid premature convergence and can achieve a better balance between exploration and exploitation. This paper presents a new training method based on hybrid particle swarm optimization with Multi-verse optimization based on Lévy flight (PLMVO) to optimize the number of hidden neurons and connection weights simultaneously in feedforward neural networks (FFNN). The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. To evaluate the proposed algorithm we used three experimental series. In the first one, the proposed PLMVO algorithm is compared with the MVO and PSO algorithms to solve a set of 15 benchmark functions to find the global solution. Meanwhile, in the second experiment, the performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using nine bio-medical datasets. The results of the comparative study show that PLMVO outperformed other training methods in most datasets and can be an alternative to other training methods. In the third experiment, the proposed PLMVO-MLP is used to predict malicious executable Linux files. The implemented model achieved very promising results with very high accuracy of 1.0, and an average f-measure of 1.0. [ABSTRACT FROM AUTHOR]

Nowadays, the power system security investigation performs a vital part so as to step up the system security with a view to keep at bay the prospect of system breakdown scenarios. With the ever-zooming power transfer, the hassles encountered by the power system have assumed alarming proportions of added intricacy with dwindling security of the power system in the power industry. In the paper, an enhanced optimal technique is proposed for solving the optimal power flow (OPF) issues with uncertainties. An enhanced technique is the Yin–Yang Pair Optimization algorithm, which is based on maintaining a balance between exploration and exploitation of the search space. Initially, the OPF is analyzed with the N–R approach and after that, the FACTS devices-based power flow is analyzed. The FACTS devices are considered as STATCOM, UPFC and IPFC devices, after the placement of FACTS devices as contingency is relived. Initially, the multi-objective function is defined and the power losses, voltage deviation, investment cost and fuel cost are analyzed. For the OPF analysis, multi-objective functions are considered under the uncertainty period. The proposed method is tested with the IEEE 30- and IEEE 118-bus system, and the corresponding line loading, line outage, generator outage, both outage and bus voltage impact are analyzed. To verify the performances of the proposed method, it is compared with the traditional methods such as Particle Swarm Optimization, Firefly Algorithm, ant Lion Optimization and Bat Optimization. The proposed method is implemented in MATLAB platform and tested with the various cases. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods. [ABSTRACT FROM AUTHOR]

In the large scale Single machine total weighted tardiness scheduling problem (SMTWTSP) is a group of not-interrelated tasks having different parameters to be executed on one machine. The problem's objective is to identify the minimum total weighted tardiness using a newly developed variant of the particle swarm optimization (PSO) algorithm. In the past, the PSO algorithm has proved itself as an efficient swarm intelligence based strategy to solve complex combinatorial problems. Here, in this article, the lunar cycle inspired local search technique is assimilated into PSO, and the designed PSO variant is termed as lunar cycle inspired PSO (LCPSO). The performance of the designed LCPSO is tested over 25 large SMTWTSP instances of job size 1000. The reported results show that the designed LCPSO is a competitive PSO variant that can be applied to provide an effective solution for the SMTWTSP type combinatorial optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, an optimal wavelet operated single index-based relay is proposed for power transmission line protection. Apart from existing usage of wavelet transform in fault detection studies, swarm assistance is provided for optimal setting of threshold values for accomplishing fast and reliable detection of faults. For this threshold assistance, particle swarm optimization (PSO) is used. Here, three individual instantaneous current signals of three phases is processed through discrete wavelet transform and based on extraction of frequency coefficients, a unique fault detection index is designed. This way of threshold setting reduces excessive computational burden and produces 100% reliable results without violating accuracy during normal and extreme operating conditions of faults in a quick time. The performance of proposed method is evaluated by its own swarm intelligence in the process of optimal search of threshold by extensive case studies. This is not possible with manual checking of threshold by random case studies. All the results are carried out in MATLAB/SIMULINK environment. [ABSTRACT FROM AUTHOR]

The utilization of nature-inspired algorithms for logistic domains and its potential to manage uncertainty evolve solutions and conduct optimization leftovers to be an antecedent research domain. The present article has considered primary bio-inspired processes to solve the logistics problem systematically until Feb 2020. We have opted 36 articles to summarize and review. The significant algorithm has been ranked into five primary categories: ant colony optimization, particle swarm optimization, memetic algorithm, genetic algorithm, and artificial bee colony. It is evident from the outcomes that within the past 10 years, bio-inspired procedures have experienced fast progress. They have prosperously been applied for optimization and design of particularly intricate systems like logistics distribution systems. It can be deducted from the outcomes that the other algorithms in the logistics distribution's optimization have to be enhanced, leading to the elevation of the effectiveness of logistics enterprises. Also, the policy assistance for the logistics organization has been empowered to boost the efficient and healthy progress of it. We can observe that the genetic algorithm and its hybrids have indicated the best efficiency until now. So, it is essential to investigate the logistics distribution route optimization by recent nature-inspired algorithms for optimizing the logistics and selecting a rational distribution scheme. [ABSTRACT FROM AUTHOR]

This paper presents an optimal sitting and sizing model of a lithium-ion battery energy storage system for distribution network employing for the scheduling plan. The main objective is to minimize the total power losses in the distribution network. To minimize the system, a newly developed version of cayote optimization algorithm has been introduced and validated based on different test functions and verified by some well-known meta-heuristics. The proposed algorithm is then applied to four different case studies in the presence of two photo voltaic (PVs) and without them. The optimization of the problems is applied based on two different trends including simultaneous and step-by-step optimization. Final results are compared with some meta-heuristics including cuckoo optimization algorithm, lion optimization algorithm, and cooperative random particle swarm optimization algorithm to show the algorithm higher efficiency. The results also showed that the presence of PVs reduces the total power losses and applying the simultaneous optimization gives higher efficiency with less total power losses. [ABSTRACT FROM AUTHOR]

This paper investigates a practical scheduling problem for radiotherapy patients, who are to be scheduled on different devices at different times. The treatment duration is increasing with time because of the continuously decaying effect of the radiation source, which also results in the decline of the serving ability. Therefore, the maintenance activity (replacing radiation source) is necessary to maintain the serving ability of medical institutions. The problem is to determine the schedule of all treatments and also when to have a maintenance activity, so as to minimize the maximum completion time of all the treatments on all devices. The lower bound of the problem is given in this paper. We prove that the optimal solution of the subproblem, i.e., scheduling patients on a single device, is independent of the sequence of the patients and is only related to the division of patients who are assigned before and after the maintenance, and thus, the subproblem can be converted to a two-partition problem. An improved dynamic programming algorithm is proposed to obtain an optimal scheme for this subproblem and its performance is better than other approaches. For multiple-device problem, an effective hybrid algorithm Gaussian crow search algorithm (GCSA) combined with crow search algorithm (CSA) and Gaussian distribution is proposed to assign all patients to different treatment devices. Finally, computational experiments demonstrate the effectiveness and stability of the proposed GCSA which is compared with CSA, simulated annealing (SA) and particle swarm optimization (PSO). The comparison results show that GCSA outperforms other algorithms in a feasible time. [ABSTRACT FROM AUTHOR]

A new attempt of employing salp swarm algorithm (SSA) to tackle the optimal power flow (OPF) problem is demonstrated in the current study. This aforementioned problem has four fitness functions to be optimized such as (1) the sum of generating units' fuel costs, (2) total network real power losses, (3) entire sum of voltage deviation of load buses, and (4) static voltage stability (VS) of electric power systems. At initial stage, these objective are solved one by one, and at a later stage, different vector objective functions are solved simultaneously by the SSA. The VS study based on a modal analysis is taken into consideration as an objective function. In this issue, the eigenvalues and eigenvectors of a reduced Jacobian matrix due to the reactive power change are figured. The smaller magnitude of eigenvalues indicates the vicinity to system voltage instability. As the magnitude of eigenvalues increases, the incremental voltage decreases, which means strong VS. The output active power of generating units, their voltages, transformers tap setting, and capacitor devices represent the search field. Two electric grids such as IEEE 57- and 118-bus electric networks are demonstrated to examine the performance of the SSA. The effectiveness of the SSA–OPF methodology is compared with that obtained by using other competing optimization methods. Furthermore, statistical performance measures comprising parametric and nonparametric tests are made and the simulation results are extensively verified which indicate a competition of the SSA with others algorithms in solving the OPF problem. [ABSTRACT FROM AUTHOR]

In order to optimize supply chain scheduling problem in mass customization mode, the mathematical programming of supply chain scheduling optimization problem is modelled. At the same time, model mapping is defined as a directed graph to facilitate the application of intelligent search algorithms. In addition, the features of genetic algorithm and particle swarm algorithm are introduced. Genetic algorithm has a strong global search capability, and particle swarm optimization algorithm has fast convergence speed. Therefore, the two algorithms are combined to construct a hybrid algorithm. Finally, the hybrid algorithm is used to solve the supply chain optimization scheduling problem model. Compared with other algorithms, the results show that the hybrid algorithm has better performance. The mathematical programming model used in this paper can be further extended and improved. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage assembly flowshop scheduling model with time cumulated learning effect, which exists in many realistic scheduling settings. By the time cumulated learning effect, we mean that the actual job processing time of a job depends on its scheduled position as well as the processing times of the jobs already processed. The first stage consists of two independently working machines where each machine produces its own component. The second stage consists of a single assembly machine. The objective is to identify a schedule that minimizes the total completion time of all jobs. With analysis on the discussed problem, some dominance rules are developed to optimize the solving procedure. Incorporating with the developed dominance rules, a dominance rule and opposition-based particle swarm optimization algorithm (DR-OPSO) and branch-and-bound are devised. Computational experiments have been conducted to compare the performances of the proposed DR-OPSO and branch-and-bound through comparing with the standard O-PSO and PSO. The results fully demonstrate the efficiency and effectiveness of the proposed DR-OPSO algorithm, providing references to the relevant decision-makers in practice. [ABSTRACT FROM AUTHOR]

An increased use of variable generation technologies such as wind power generation can have important effects on system frequency performance during normal operation as well as contingencies. This has led to new challenges for system operators in terms of improving frequency characteristics during contingencies. This subject is stated within the framework of frequency-constrained unit commitment problem (FCUCP) in this study by augmenting the frequency characteristic in the multi-thermal units. The ramp rate is proposed as dynamic constraint, and FCUCP is solved efficiently by binary particle swarm optimization (BPSO) based heuristic optimization algorithms. The influence of the proposed ramping on FCUCP is simulated, and it can be shown that the costs of the system under considering practical limitations that are solved by BPSO are minimized. [ABSTRACT FROM AUTHOR]

The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). In this new algorithm, the Tent chaotic sequence is used to initiate the individuals' position, which can increase the diversity of the wolf pack. And the nonlinear control parameter is used to balance the global search and local search ability of the algorithm and improve the convergence speed of the algorithm. At the same time, the idea of PSO is introduced, which utilize the best value of the individual and the best value of the wolf pack to update the position information of each grey wolf. This method preserves the best position information of the individual and avoids the algorithm falling into a local optimum. To verify the performance of this algorithm, the proposed method is tested on 18 benchmark functions and compared with some other improved algorithms. The simulation results show that the proposed algorithm can better search global optimal solution and better robustness than other algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method based on the solution of the nonlinear Riccati equation for the design of coordinated, decentralized damping controllers for power systems. While other approaches use analytical methods that ensure robustness with respect to uncertainties in the system operating conditions by considering a controller of the same order as the plant, which results in high-order controller structures, the proposed method has the advantage of providing robust and decentralized low-order controllers. The method is applied to two IEEE benchmarks, and the designed controllers are assessed by modal analysis and nonlinear time-domain simulation. [ABSTRACT FROM AUTHOR]

In data centers are provided solution to the consumer and to the organization by means of store and process their data. When scheduling operation carrying more requirements for resources than it can hold, in this situation load balancing strategy distributes workloads across multiple servers to optimize the performances. However, resource allocation and load balancing is an inspiring problem for the cloud service providers to consumers in terms of Quality of Services. The proposed hybrid bacterial swarm optimization algorithm, achieve global seek over the entire search space through PSO while local search is achieved by BFO algorithm. This paper proposed a novel idea, how to tackle the scheduling problem by using hybrid load balancing techniques. The experimental results demonstrate that the projected algorithms overtake the existing SA, PSO, Dynamic ADS algorithms considerably by minimizing the operational cost, make-span and maximize the utilization of the resource. [ABSTRACT FROM AUTHOR]

Proportional, integrative and derivative (PID) controllers are among the most used in industrial control applications. Classical PID controller design methodologies can be significantly improved by incorporating recent computational intelligence techniques. Two techniques based on particle swarm optimization (PSO) algorithms are proposed to design PI-PID controllers. Both control design methodologies are directed to optimize PI-PID controller gains using two degrees-of-freedom control configurations, subjected to frequency domain robustness constraints. The first technique proposes a single-objective PSO algorithm, to sequentially design a two degrees-of-freedom control structure, considering the optimization of load disturbance rejection followed by set-point tracking optimization. The second technique proposes a many-objective PSO algorithm, to design a two degrees-of-freedom control structure, considering simultaneously, the optimization of four different design criteria. In the many-objective case, the control engineer may select the most adequate solution among the resulting optimal Pareto set. Simulation results are presented showing the effectiveness of the proposed PI-PID design techniques, in comparison with both classic and optimization based methods. [ABSTRACT FROM AUTHOR]

Hypoglycemia is a common and serious side effect of type 1 diabetes. We measure physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients using a multiple regression-based combinational neural logic approach. In this work, a neural logic network with multiple regression is applied to the development of non-invasive hypoglycemia monitoring system. It is an alarm system which measures the physiological parameters of electrocardiogram signal (heart rate and corrected QT interval) and determine the onset of hypoglycemia by the use of proposed hybrid neural logic approach. In this clinical application, a combinational neural logic network with multiple regression is systematically designed to hypoglycemia detection based on the characteristic of this application. To optimize the parameter of the hybrid combinational neural logic system, hybrid particle swarm optimization with wavelet mutation is applied to tuned the parameters of the system. To illustrate the effectiveness of the proposed method, hypoglycemia monitoring system which will be practically analyzed using real data sets collected from 15 children ( $$14.6 \pm 1.5$$ years) with type 1 diabetes at the Department of Health, Government of Western Australia. With the use of proposed method, the best testing sensitivity of 79.07 % and specificity of 53.64 % were obtained. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology to tune power system stabilizers and thyristor-controlled series capacitor damping controllers simultaneously. The particle swarm optimization algorithm is incorporated into a power system model to tune the parameters of supplementary damping controllers. A test power system of 10 generators, 39 buses and 46 transmission lines is simulated to validate the use of this optimization algorithm. The tuning of supplementary damping controllers using the proposed methodology increases their performance to provide additional damping to low-frequency oscillation modes in the simulated power system. The controller position is determined by the participation factors (power system stabilizers) and the distance between the interest pole and the zero of the open-loop transfer function of the power oscillation damping controller (thyristor-controlled series capacitor-power oscillation damping). The results show the operating efficiency of the power system after using the optimization technique to tune damping parameters, thereby improving power system integrity. The power sensitivity model is used for the simulations presented in this work focusing on the analysis small-signal stability. [ABSTRACT FROM AUTHOR]

Procurement lot-sizing and production scheduling are as the two critical factors on controlling system costs. This paper considers a particular problem of integrated lot-sizing and scheduling for several products in capacitated flexible job shop configuration, taking into account sequence-dependent setup time. First, a novel mixed integer programming (MIP) model, based on big bucket time models, is proposed to formulate the problem. Then, in order to overcome the complexity of this model, a new hybrid algorithm which combines the genetic algorithm (GA), particle swarm optimization algorithm (PSO), and a local search heuristic is developed. The applicability of GA to solving problems with discrete variables and the efficacy of PSO to tackling problems with continuous variables is the motivation for applying the combination of these algorithms to the investigated problem which has both discrete and continuous solution space. The Taguchi method is used in order to calibrate the simulated annealing algorithm parameters. Then, the efficiency of the proposed algorithms is discussed. The computational results indicated that the proposed algorithm has performed better than the classic GA algorithm and MIP model with respect to both the quality of solutions and computation time. [ABSTRACT FROM AUTHOR]

A novel chaotic search method is proposed, and a hybrid algorithm combining particle swarm optimization (PSO) with this new method, called CLSPSO, is put forward to solve 14 integer and mixed integer programming problems. The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods. Experimental results indicate that in terms of robustness and final convergence speed, CLSPSO is better than other five algorithms in solving many of these problems. Furthermore, CLSPSO exhibits good performance in solving two high-dimensional problems, and it finds better solutions than the known ones. A performance index (PI) is introduced to fairly compare the above six algorithms, and the obtained values of (PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions. [ABSTRACT FROM AUTHOR]

In factories during production, preventive maintenance (PM) scheduling is an important problem in preventing and predicting the failure of machines, and most other critical tasks. In this paper, we present a new method of PM scheduling in two modes for more precise and better machine maintenance, as pieces must be replaced or be repaired. Because of the importance of this problem, we define multi-objective functions including makespan, PM cost, variance tardiness, and variance cost; we also consider multi-parallel series machines that perform multiple jobs on each machine and an aid, the analytic network process, to weight these objectives and their alternatives. PM scheduling is an NP-hard problem, so we use a dynamic genetic algorithm (GA) (the probability of mutation and crossover is changed through the main GA) to solve our algorithm and present another heuristic model (particle swarm optimization) algorithm against which to compare the GA's answer. At the end, a numerical example shows that the presented method is very useful in implementing and maintaining machines and devices. [ABSTRACT FROM AUTHOR]

The global energy sector faces major challenges in providing sufficient energy to the worlds ever-increasing energy demand. Options to produce greener, cost effective, and reliable source of alternative energy need to be explored and exploited. One of the major advances in the development of this sort of power source was done by integrating (or hybridizing) multiple different alternative energy sources (e.g., wind turbine generators, photovoltaic cell panels, and fuel-fired generators, equipped with storage batteries) to form a distributed generation (DG) power system. However, even with DG power systems, cost effectiveness, reliability, and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system were carried out successfully in the previous work using particle swarm optimization (PSO). The goal was to minimize cost, maximize reliability, and minimize emissions (multi-objective function) subject to the requirements of the power balance and design constraints. In this work, the optimization was performed further using Hopfield neural networks (HNN), PSO, and HNN-PSO techniques. Comparative studies and analysis were then carried out on the optimized results. [ABSTRACT FROM AUTHOR]