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With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is considered as one of Maximum Power Point Tracking (MPPT) controller algorithm developed for PhotoVoltaic system (PV) to guarantee a maximum power extraction under different climatic conditions of temperature and irradiation. Classical MPPT algorithms like Perturbe and Observe (P&O), PSO, Adaptive Neuro-Fuzzy Inference System (ANFIS) are an effective method for tracking the maximum power point (MPP) for the PV systems, but the problems with these approaches that they are less stable, high oscillation around steady state and slower convergence to the MPP. Based on recent research, the purpose of this paper is to introduces a novel MPPT controller based on a modified version of heterogeneous multi swarm PSO algorithm using an adaptive factor selection strategy (FMSPSO), to overcome the previous shortcomings and compared with conventional PSO, ANFIS and classical P&O controllers. Simulation and experimental results revealed that the new FMSPSO algorithm can overcome the previous shortcomings providing the superior performance to track the MPP efficiently with a shorter convergence time and small oscillations compared to other algorithms. The experimental confirmation of the FMSPSO algorithm has been carried out using NI-myRIO-1900 card and shows that with the proposed MPPT approach efficiency can reach a value greater than 99% even in climatic variation of irradiation and temperature. • A classical MPPT controller cannot track the dynamic the MPP, has obvious oscillations, and converges slowly. • A novel MPPT controller based on a modified version of MSPSO algorithm using an adaptive factor selection strategy (FMSPSO) is proposed. • A FMSPSO based MPPT algorithm is compared with conventional PSO, classical P&O and ANFIS controllers for validation. • The proposed FMSPSO algorithm provides the superior performance by boosting the PV system tracking efficiency more than 99%. [ABSTRACT FROM AUTHOR]

This paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches Algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases including avalanche due to mountain slope, human factors, weather in the region as well as normal conditions and it has only one control parameter. The advantages of this algorithm are low control parameters, simple structure and also easy implementation. The effectiveness of the SAA algorithm is examined on 23 classic benchmark test functions. Then, the effectiveness of the SAA to achieve accurate results in different aspects is examined and proved on engineering problems including six different cases. The superiority of the SAA to solve the classic benchmark test functions is compared with spotted hyena optimization (SHO), particle swarm optimization (PSO), Aquila optimizer (AO), differential evolution (DE), bat algorithm (BA), dwarf mongoose optimization (DMO), genetic algorithm (GA), artificial bee colony (ABC), and ant colony optimization (ACO). The simulation results provide evidence for the well-organized and efficient performance of the SAA in solving a great diversity of engineering problems. The results demonstrated that the SAA can be more effective than other algorithms to solve the test functions in terms of optimization accuracy and convergence rate. Moreover, the results proved that the SAA obtained more competitive results than the previous methods to solve constrained engineering optimization problems, especially hybrid energy system design as well as economic load dispatch problems. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) can ensure the network security by identifying network intrusions according to the abnormal traffic data. However, the intrusion detection data has the problem of high dimensionality and changes with network and attack environments, which leads to the poor performance and poor portability of intrusion detection algorithms. Therefore, this paper proposes an intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network. Firstly, a feature selection method based on symmetric uncertainty and approximate Markov blanket is proposed, which fully considers the correlation and redundancy of features, and also the correlation between combined features and the class label, so as to reduce the data dimensionality. Secondly, the CNN-LSTM classifier fused with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to extract the spatial features and temporal features to improve the classification performance. Finally, the Particle Swarm Optimization (PSO) algorithm is improved and used to automatically optimize the hyperparameters of the classifier, so that the classifier can be applied to different intrusion detection datasets with better generalization ability and portability. Experiments have verified the effectiveness and superiority of the proposed algorithm on multiple evaluation indicators. • An effective algorithm for intrusion detection is proposed in this paper. • Feature selection is based on symmetric uncertainty and approximate Markov blanket. • A fusion neural network is constructed to extract the spatial and temporal features. • The PSO algorithm is improved to automatically optimize the hyperparameters. [ABSTRACT FROM AUTHOR]

• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

The focus of this paper is to determine how the mechanisms of each algorithm contribute to search performance. swarm-based algorithms are composed of several mechanisms. This paper regards these independent mechanisms as modules. The modules constituting each algorithm are classified into three categories: (1) the common module: a collection of mechanisms that all swarm-based algorithms have in common; (2) the main module: the unique mechanisms of each algorithm that updates the solution by interacting with other solutions; and (3) the sub module: the unique mechanisms of each algorithm that do not directly improve the quality of the solution. Testing the algorithms combined into these modules reveals the contribution of each module to performance. The experiments are conducted for the three algorithms PSO, ABC, and CS to test the usefulness of this concept. The results of these experiments revealed the following implications: (a) the main module of ABC alone can have good performance; (b) the main module of CS does not perform well as a stand-alone; (c) the combination algorithm with the main module of PSO and the sub module of ABC can improve applicability. [ABSTRACT FROM AUTHOR]

Accurate electric energy metering in micro-grid is one of the most urgent problems in the field of electric energy metering. In order to solve the problem that the lackness of electric energy metering can't be accurately calculated with the existing electric energy compensation algorithm when the electric energy metering device is completely faulty, this paper proposes an electric energy compensation algorithm that is limited in micro-grid and is based on standard particle swarm to optimize the deep belief network. Taking the historical electric energy data of each micro source metering point in micro-grid as an example, it is divided into the training set and the test set, the standard particle swarm optimization algorithm is used to determine the optimal learning layers and learning rate of deep belief network, and the electric energy algorithm model is established. The model verification results show that the proposed algorithm is nearly five times lower than the algorithm based on deep belief network solely. Further engineering case comparison shows that lackness of electric energy metering compensated by the algorithm studied in the paper is closer to the theoretical value, and the error is also less than the traditional average electricity algorithm. The real-time power curve is closer to the theoretical value, and has the higher accuracy. [ABSTRACT FROM AUTHOR]

Classification is a common problem in various fields of life, and the key challenging task in data mining. The primary objective of the classification process is to classify the given dataset in a defined class label for all data. Many research papers widely used classification in the medical sector. Various researches have been carried out to classify medical data using different techniques, such as High Order Neural Network (HONN) combined with Back-Propagation Neural Network (BPNN) as a learning algorithm. Due to the increased data complexity, the back-propagation algorithm faced problems of slow convergence. The Back-Propagation (BP) using the gradient descent technique has the possibility of getting stuck in local minima. So, the BP algorithm may cause difficulties in finding the global minima of the error function. This paper proposed a high-order Functional Link Neural Network (FLNN). The proposed FLNN model is integrated with a metaheuristic-based searching algorithm called Accelerated Particle Swarm Optimization (APSO). The performance of the proposed Accelerated Particle Swarm Optimization Functional Link Neural Network (APSOFLNN) model is validated with various medical datasets and compared to traditional techniques such as Accelerated Particle Swarm Optimization Functional Link Back-Propagation (APSOFLBP), and Artificial Bee Colony Functional Link Neural Network (ABCFLNN). The simulation results showed that the proposed APSOFLNN algorithm for the used benchmarked datasets shows good results in terms of the mean square error (MSE) and accuracy in comparison to the traditional techniques. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) provides a metaheuristic optimization technique for real-life functions with complex behavior. Inspired by the metaphor of social behavior as exhibited by swarms operating in natural surroundings, the algorithm captures the Emergent Complexity demonstrated by individual components of large groups which work together using simple rules and create complex, yet efficient, adaptive, and self-organizing systems. The paper seeks to rigorously analyze the hyperparameters of the algorithm to find the relative importance of cognitive(personal) vis-à-vis social (swarm) parameters of the algorithm so as to gain a perfect understanding of the importance of exploitation versus exploration parameters thus enabling us to enhance the application efficiency of the algorithm. The paper further suggests and applies a rolling coefficient updating technique to the algorithm parameters so as to use a formal statistical procedure in the initialization and subsequent iterations for updating coefficients rather than leaving it on trial and error or on a practitioner's knowledge. Results suggest that the technique employed provides an enhancement in the efficiency of the algorithm by creating a dynamic Emergent Complexity environment. [ABSTRACT FROM AUTHOR]

The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07. • The development of an adaptive algorithm for data compression is applied in power system data. • MO-PSO algorithm is proposed to find the optimal threshold in WT based data compression. • Results are compared with recent research works. • The efficiency of data compression algorithm is evaluated by using compression ratio, mean square error and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Aiming at the problems of single optimization strategy and poor comprehensive performance of MOFA, multi-objective firefly algorithm with adaptive region division is proposed in this paper. By leveraging the convergence index, our algorithm intelligently divides the dominant and non-dominant solution groups into three sub-regions, namely balance, exploration, and development areas, each with a distinct learning strategy that complements the strengths of fireflies. Specifically, fireflies in the balance area learn from global optimal particles with diversity to achieve a balanced exploration and development ability. Fireflies in the exploration area jointly learn from globally optimal particles with convergence and diversity, increasing the algorithm's likelihood of discovering Pareto optimal solutions. Lastly, fireflies in the development area rapidly converge under the guidance of the globally optimal particle of convergence, thus improving the algorithm's development ability. To further enhance the comprehensive optimization performance, we introduce a novel fusion index as an external archive update strategy that preserves solutions with superior convergence and diversity. Our experiments on 20 benchmark functions and a multi-objective optimization power flow example demonstrate that our algorithm outperforms other multi-objective optimization algorithms, highlighting its superior optimization performance. • This paper proposes a multi-objective firefly algorithm with adaptive region division. • Based on the idea of adaptive region division, the firefly population was divided into adaptive regions and regions, and multiple learning strategies were integrated. • Our algorithm has higher performance than the classical MOEA on instances of benchmarking functions and multi-objective optimization currents. [ABSTRACT FROM AUTHOR]

The Pichia pastoris fermentation process is with highly nonlinear and time-varying and strong coupling characteristics, traditional control methods such as classic PID cannot satisfy the actual needs of the fermentation process control. In order to effectively solve the control problem of the Pichia pastoris fermentation process, a multi-model predictive control method is presented based on the relative error weighting algorithm. First, the prior sample data are divided into multiple training sample sets (sample cluster) by fuzzy C-means clustering algorithm (FCM). Then, the corresponding sub prediction model is obtained by using least square support vector machine (LSSVM) and Improved particle swarm optimization (IPSO) algorithm for each sample cluster. Finally, the control strategy of predictive model is constructed based on multi model relative error weighting algorithm. The simulation in the Pichia pastoris fermentation process shows that the algorithm proposed in this paper could improve the transient response and perform good output tracking in a wide range. It improves the adaptive ability of the model and makes it more accurate to describe the actual state of the nonlinear system. [ABSTRACT FROM AUTHOR]

Being sensitive to initial position and noise is the key problem that must be solved in point cloud registration. To improve the overall performance of registration methods, a point cloud registration method based on the Dark Forest Algorithm is proposed in this paper. The introduction of swarm intelligence enhances the adaptability of the registration method to the initial position and improves the robustness of the point cloud registration system. Furthermore, the application of the Dark Forest Algorithm with four capability enhancement strategies: the hierarchical elite, civilization self-decision, suspicion chain, and technology explosion has improved the comprehensive competitiveness in point cloud registration. The mentioned strategies have brought a great positive impact on the registration methods in solving the far initial position, breaking away from the local optimal solution, and weakening the noise interference. The paper aims to provide the Dark Forest Algorithm and the point cloud registration method based on it, which are elaborated on in detail and compared to other existing algorithms to prove the comprehensive performance, respectively. Then, the point cloud registration experiment for coal-wall point clouds was carried out and the results indicate that the proposed registration algorithm is better than other algorithms for standard point cloud dataset and coal-wall point cloud, which demonstrates that the proposed registration method has strong potential competitiveness. • Proposed a point cloud registration based on a novel meta-heuristic algorithm. • Dark Forest Algorithm is proposed with the higher accuracy and stronger adaptability. • Cloud registration based on the DFA demonstrates potential competitiveness. [ABSTRACT FROM AUTHOR]

Extracting relevant information from a large amount of data is a challenging task. Automatic text summarization is a potential solution for obtaining this information. In this paper, a nature inspired swarm intelligence-based algorithm viz. firefly algorithm for multi-document text summarization is proposed. A new fitness function consisting of three features viz. topic relation factor, cohesion factor and readability factor is utilized. The experiments are performed on datasets from Document Understanding Conference i.e. DUC-2002, DUC-2003 and DUC-2004. The performance of the algorithm has been evaluated using ROUGE score. The performance of the proposed algorithm is compared with some other nature inspired ones such as particle swarm optimization (PSO) and genetic algorithm (GA). The performance of the proposed algorithm outperforms the other adopted ones. [ABSTRACT FROM AUTHOR]

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. [ABSTRACT FROM AUTHOR]

Fractional order filters offer greater freedom of design and a precise control over stopband attenuation in electronic circuits and systems. This paper presents the design of a fractional order low pass Chebyshev filter (FOLCF) that achieves near-ideal response characteristics. The methodology introduced utilizes metaheuristic optimization methods, including particle swarm optimization, firefly algorithm, and grey wolf optimization. These techniques are employed to precisely adjust the filter coefficients for the orders (1+α), (2+α), and (3+α). The adjustment is carried out by comparing the desired behaviour of the FOLCF with generalized fractional order low pass transfer functions. Throughout these instances, the parameter α is varied within the range of (0, 1). The designed filters are then tested and compared on the basis of various factors. Simulation results demonstrate that the designed filters closely follow the behaviour of an ideal Chebyshev filter with maximum passband and stopband magnitude errors being −31.93 dB and −52.74 dB respectively for (1+α) order filters. These values for (2+α) and (3+α) order FOLCF have been observed to be −30.04 dB and −55.91 dB; −17.72 dB and −49.52 dB respectively. Furthermore, it has been observed that the proposed work outperforms existing state-of-the-art approaches in various aspects, including magnitude error, stopband attenuation, and cut-off frequency. The stability of the designed filters has been verified through stability analysis. Additionally, practical feasibility of the proposed FOLCF is demonstrated through SPICE simulations for α = [0.2,0.5,0.8] using second generation current conveyor (CCII) and operational transconductance amplifier (OTA) based topologies while approximating the constant phase element using fifth order continued fraction expansion. The SPICE implementations closely follow the behaviour of ideal filter with −48.67 dB and −62.8 dB as mean square errors for CCII and OTA circuits respectively, showcasing the proposed filters' superiority and practical applicability in advanced electronic design. • A generalized design of Fractional order low Pass Chebyshev filters having near idle response has been proposed. • Nature inspired Algorithms: particle swarm optimization (PSO), firefly algorithm (FA) & grey wolf optimization (GWO) are used. • Comparisons with existing literature on basis of magnitude errors, stopband attenuation and cut off frequencies has been made. • Stability analysis for all the designed filters has been done by converting s-plane to W-plane. • Realization of proposed filters has been done by using CCII and OTA based circuits to test feasibility. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in the theoretical research of existing meta-heuristic algorithms and in the development of novel algorithms. However, the research on the definition and construction methods of the so-called "mutation" operator, a concept widely used in existing meta-heuristic algorithms, remains notably insufficient. In addition, how to directly construct a meta-heuristic algorithm under the guidance of stability and invariance analysis continues to pose a challenge that has yet to resolve. This paper carefully investigates and rigorously defines the concepts of "mutation operator" and "mutation-driven meta-heuristic algorithm" as found in the literature. According to these definitions, a novel meta-heuristic algorithm, called Multi-direction guided Mutation-driven Stable swarm Intelligence algorithm with Translation and Rotation invariance (M2SITRI) is proposed, and the corresponding mutation operator is carefully designed based on the analysis of translation invariance, rotation invariance, order-1 stability, and order-2 stability. The stability and invariance properties of the M2SITRI algorithm are validated through numerical simulations, and its superior solving performance is demonstrated by benchmarking it against several other well-known existing meta-heuristic algorithms across a variety of test problems. • Mutation operator and mutation-driven meta-heuristic algorithm concepts are defined. • A novel translation and rotationally invariant meta-heuristic algorithm termed M2SITRI is proposed. • The invariance and stability properties and the excellent solving performance of M2SITRI algorithm are verified. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the major issues in cloud computing environment. Efficient task scheduling is substantial to attain cost-effective execution and improve resource utilization. The task scheduling problem is classified to be a nondeterministic polynomial time (NP)-hard problem. This feature attracts researchers to utilize nature inspired metaheuristic algorithms. Initializing searching solutions randomly is one of the key features in such optimization algorithms. However, assisting metaheuristic algorithms with effective initialized solutions can significantly improve its performance. In this paper, an improved initialization of particle swarm optimization (PSO) using heuristic algorithms is proposed. Longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms are used to initialize the PSO. The performance of the proposed LJFP-PSO and MCT-PSO algorithms are evaluated in minimizing the makespan, total execution time, degree of imbalance, and total energy consumption metrices. Moreover, the performance of the proposed algorithms is compared with recent task scheduling methods. Simulation results revealed the effectiveness and superiority of the proposed LJFP-PSO and MCT-PSO compared to the conventional PSO and comparative algorithms. [ABSTRACT FROM AUTHOR]

Load balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machines (VM) should have balanced load to achieve optimal utilization of its capabilities. This paper proposes a novel methodology of dynamic balancing of load among the virtual machines using hybridization of modified Particle swarm optimization (MPSO) and improved Q-learning algorithm named as QMPSO. The hybridization process is carried out to adjust the velocity of the MPSO through the gbest and pbest based on the best action generated through the improved Q-learning. The aim of hybridization is to enhance the performance of the machine by balancing the load among the VMs, maximize the throughput of VMs and maintain the balance between priorities of tasks by optimizing the waiting time of tasks. The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. The comparison of the simulation and real platform result shows our proposed algorithm is outperforming its competitor. [ABSTRACT FROM AUTHOR]

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time. • The pricipal controls of the wind turbine system are are the MPPT and the pitch angle. • The authers proposed the Type-2 Fractional order PID controller based on salps swarm algorithm. • The results proved the superiority of proposed controller than the classical PID controller and others are largely suggested in the literature. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (d FDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the d FDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (d FDBARO) algorithm. Moreover, the practicability of the proposed d FDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed d FDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the d FDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Natural frequencies, which are relatively easy parameters to obtain, provide efficient information about the dynamic behavior of structures. Controlling these parameters can help to minimize the destructive effect of dynamic loads on structures. However, the optimal design of the size and shape of truss structures with controlling dynamic constraints is an expensive problem. While frequency constraints are nonlinear and non-convex, handling natural frequencies as constraints is a challenging task, which prevents the phenomenon of resonance, large deformations, and destruction of a structure. Also, reducing the vibration amplitude of a structure would reduce the stress and deflections. In this paper, the Improved Marine Predators Algorithm (IMPA) is proposed and implemented for size and shape optimization of truss structures subject to natural frequency constraints. In this method, a Novel CF (NCF) factor is introduced to control the predator's step size when looking for prey. To evaluate the performance of IMPA, the optimum weight of structures under dynamic constraints was computationally investigated. To demonstrate the efficiency and robustness of IMPA, 37-bar truss bridge, 52-bar dome, 72-bar space truss, 120-bar dome, 200-bar planar truss, and 600-bar dome truss were optimized. Compared to other state-of-the-art algorithms, the results indicate that IMPA performs better in solving these nonlinear structural optimization problems. • Marine Predators Algorithm Improved based on a new strategy. • A novel formula is proposed for CF factor. • engineering problems with dynamic constraints are investigated to prove the capabilities of new swarm algorithm. • The results are compared with state-of-the-art swarm algorithms. [ABSTRACT FROM AUTHOR]

Particle Swarm (PSO) and Bacterial Foraging (BF) Optimizers are two widely used optimization techniques. A proper combination of these two algorithms would improve their search capability while minimizing their shortcomings, such as parameter dependency and premature convergence. This paper presents a hybrid optimization algorithm that combines PSO and BF (HPSBF) to ensure security and the system's stability following faults and disturbances. The formulated objective function is claimed to be innovative and straightforward. The set objectives are to minimize the dropped load by shedding relays while maximizing the lowermost swing frequency. The optimal operation of Under-Frequency Load-Shedding (UFLS) Relays is driven by the HPSBF technique as a bounded optimization with bounds representing the limits of the system's state variables. The viability of the HPSBF is verified against conventional-, PSO-, and BF-UFLS approaches. The standard IEEE 9-bus and IEEE 39-bus systems are exploited to examine the response of the developed UFLS techniques. The tested systems are exposed to various operational scenarios such as loss of power plants and a considerable abrupt load increase. The DigSilent power factor software is used to simulate the IEEE 9- and 39-bus systems, while MATLAB code was implemented to obtain optimal operational points for the implemented algorithms. The HPSBF accomplished the uppermost swing frequency and the lowermost quantity of the disconnected load. Furthermore, the computational times of HPSBF are equivalent to those of the PSO. [ABSTRACT FROM AUTHOR]

It is a common occurrence nowadays for a network to be gigantic in size and complex in architecture. Network navigation thus faces many new problems in terms of routing and resource utilization, one of which arises in multi-domain networks. This paper focuses on the Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem, whose purpose is to find the shortest path between two nodes in a network under a constraint that such a path is only allowed to traverse each domain at most once. Considering that this is an NP-Hard problem, an approximate approach is more practical than an exact one. Meanwhile, Particles Swarm Optimization Algorithm (PSO) has been long known for its powerful ability to discover near-optimal solutions in a reasonable time; however, its application in discrete search space is limited. Therefore, we decide to use the multi-population framework, a sub-branch of multitasking optimization which allows information exchange not only between problems but also between different optimization heuristics, to improve upon the basic PSO method. Specifically, this paper introduces a hybridization between PSO and Variable Neighborhood Search (VNS). The encoding and decoding method are created specifically for the IDPC-NDU problem and for the PSO algorithm, and VNS serves to enhance further the algorithm's ability to escape local optima. Experiments are carried out to prove the new algorithm's efficacy, especially in the context of similar multitasking methods. • Propose a multi-tasking technique for IDPC-NDU. • Propose novel encoding and decoding methods for the IDPC-NDU problem. • Integrate PSO and VNS into a multi-population-based approach to solve the IDPC-NDU problem. • Evaluate the efficiency of the proposed algorithm on various instances. [ABSTRACT FROM AUTHOR]

In this paper, the far-distance nonplanar rapid cooperative rendezvous with multiple specific-direction thrusts is investigated. In the optimization process, the indirect method is adopted. The homotopy method is used to smooth the dynamical equations. The solutions of the initial co-state variables of the energy-optimal problem are obtained by the quantum-behaved particle swarm optimization (QPSO) algorithm. Sequential quadratic programming (SQP) algorithm is coupled into the homotopy method to overcome the difficulty in nonsmooth integration when the thrust switches on & off frequently. The solutions of the fuel-optimal problem are obtained by the homotopy-SQP coupled method. The simulation results show the feasibility of the cooperative rendezvous with multiple specific-direction thrusts. The "suitable point" of normal thrust is investigated. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regression and particle swarm optimization algorithm. For the proposed method, the global performance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objective functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process regression and particle swarm optimization algorithm was proposed for multi-objective optimization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint; R a revolute joint; U a universal joint). The comparison from the back propagation neural network, multivariate regression, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization , the performance indices were significantly improved. • A mapping modeling between objective functions and design parameters based on Gaussian process regression is proposed. • A multi-objective optimization design method combined with the mapping model and particle swarm algorithm is proposed. • The proposed method can significantly reduce the computational cost for the high computation-intensive design problems. • A study on the comparison of the robustness and accuracy among the three mapping models is carried out. • The three mapping models are multivariate regression, BP neural network, and Gaussian process regression models. [ABSTRACT FROM AUTHOR]

Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service. This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is considered a challenging machine learning problem that handles the large size of features. The main purpose of FS is to remove irrelevant and redundant variables to improve the performance of the learning algorithms. Consequently, the FS process is considered as an optimization problem where metaheuristics approaches prove efficiency in solving it. In this paper, we propose new binary versions of a new ancient inspired metaheuristic approach called Giza Pyramids Construction (GPC) to select the most relevant subset of features. The proposed binary versions of the algorithm called BGPC-S and BGPC-V are implemented with two transfer functions, with the main objective of maximizing classification accuracy and minimizing the number of selected features. The two versions of BGPC were compared to six well-known binary metaheuristics for feature selection problem, namely Binary Atom Search Optimisation (BASO), Binary Bat Algorithm (BBA), Binary Differential Evolution (BDE), Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), and Binary Harris Hawks Optimizer (BHHO), and evaluated over 20 datasets from the UCI repository. Experiments have demonstrated that the proposed approaches outperformed the other algorithms in terms of classification accuracy and the number of selected features. [ABSTRACT FROM AUTHOR]

This paper aims at the parameter estimation of dual-rate Hammerstein-Volterra (DR-HV) systems. The auxiliary model (AM) method is applied to solve the incomplete identification data caused by the dual-rate sampling. Besides, combining the particle swarm optimization (PSO) and the gradient search principle, this paper proposes an improved algorithm — the hybrid particle swarm-gradient based on the auxiliary model (AM-HPSG) algorithm, which is employed to the DR-HV identification. The AM-HPSG method converts the nonlinear identification issue into the optimization problem in the parameter space. Moreover, the application of the gradient mutation strategy in the AM-HPSG algorithm can not only improve the optimization speed and the identification accuracy, but also solve the premature problem of the basic PSO method. To verify the feasibility of the AM-HPSG algorithm, the second-order DR-HV model and the third-order DR-HV models are considered in simulation. And it can be easy to find that the AM-HPSG performs much better than the PSO and the gradient iterative (GI) methods through algorithm comparison. [ABSTRACT FROM AUTHOR]

This paper aims to bring in some notes and improvements for the paper (Kong et al., 2015). Firstly, we give some notes and suggestions on its assumption for numbers of dispensable parameter subsets in soft sets. This helps make the theories in Kong et al. (2015) much more logical clear and consistent. Then we propose a linear programming mathematical representation for normal parameter reduction problems of soft sets. This allows us to solve normal parameter reduction problems of soft sets with Lingo or MATLAB software. By the way, we point out that the answer of Example 6.1 in Kong et al. (2015) is not correct and figure out the right answer with our linear programming method. [ABSTRACT FROM AUTHOR]

Automatic path planning is an essential aspect of unmanned aerial vehicle (UAV) autonomy. This paper presents a three dimensional path planning algorithm based on adaptive sensitivity decision operator combined with particle swarm optimization (PSO) technique. In the proposed method, an adaptive sensitivity decision area is constructed to overcome the defects of local optimal and slow convergence. By using this specified area, the potential particle locations with high probabilities are determined and other candidates are deleted to improve computational capacity. Then the searching space of particles is constrained in a limited boundary to avoid premature state. In addition, the searching accuracy is enhanced by the relative particle directivity from current location. The objective function is redesigned by taking into account the distance to destination and UAV self-constraints. To evaluate the path length, the paired-sample T-Test is performed and the straight line rate ( SLR ) index is introduced. In the two scenarios applied in this paper, our proposed method is 35.4 % , 21.6 % and 49.5 % better compared with other three tested optimization algorithms in the path cost on average. Correspondingly it is 9.6 % , 12.8 % , and 25.3 % better in SLR , which is capable of generating higher quality paths efficiently for UAVs. [ABSTRACT FROM AUTHOR]

Both the species strategy and the memory scheme are efficient methods for addressing dynamic optimization problems. However, the combination of these two efficient techniques has scarcely been studied. Thus, this paper focuses on how to hybridize these two methods. In this paper, a new swarm updating method is proposed to enhance a representative species-based algorithm, i.e., SPSO (Species-based Particle Swarm Optimization), and the new algorithm is named MSPSO. MSPSO has two characteristics. First, the number of replaced particles in the current swarm is set adaptively according to the number of species. To not substantially destroy the exploitation capability of each species, no more than one particle in each species is replaced by the memory. Second, the retrieved memory particles are categorized according to their fitness values and their distances to the seed of the closest species. Aimed at enhancing the search in both promising areas and existing species, each category is processed by different operations. The MPB, Cyclic MPB and DRPBG are used to test the performance of MSPSO. Experimental results demonstrate that MSPSO is competitive for dynamic optimization problems. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the optimal design of a grid-dependent and off-grid hybrid renewable energy system (RES). This system consists of Photovoltaic (PV), Wind Turbine (WT) as well as Fuel Cell (FC) with hydrogen gas tank for storing the energy in the chemical form. The optimal components sizes of the proposed hybrid generating system are achieved using a novel metaheuristic optimization technique. This optimization technique, called Improved Artificial Ecosystem Optimization (IAEO), is proposed for enhancing the performance of the conventional Artificial Ecosystem Optimization (AEO) algorithm. The IAEO improves the convergence trends of the original AEO, gives the best minimum objective function, reaches the optimal solution after a few iterations numbers as well as reduces the falling into the local optima. The proposed IAEO algorithm for solving the multiobjective optimization problem of minimizing the Cost of Energy (COE), the reliability index presented by the Loss of Power Supply Probability (LPSP), and excess energy under the constraints are considered. The hybrid system is suggested to be located in Ataka region, Suez Gulf (latitude 30.0, longitude 32.5), Egypt, and the whole lifetime of the suggested case study is 25 years. To ensure the accurateness, stability, and robustness of the proposed optimization algorithm, it is examined on six different configurations, representing on-grid and off-grid hybrid RES. For all the studied cases the proposed IAEO algorithm outperforms the original AEO and generates the minimum value of the fitness function in less execution time. Furthermore, comprehensive statistical measurements are demonstrated to prove the effectiveness of the proposed algorithm. Also, the results obtained by the conventional AEO and IAEO are compared with those obtained by several well-known optimization algorithms, Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). Based on the obtained simulation results, the proposed IAEO has the best performance among other algorithms and it has successfully positioned itself as a competitor to novel algorithms for tackling the most complicated engineering problems. [ABSTRACT FROM AUTHOR]

Trajectory similarity analysis of moving target is the foundation for mining high-value and regular behavioral information such as motion preferences, activity hotspots and frequent paths. Unlike most trajectory similarity analysis methods aimed at discovering correlations of target activities in time, space or spatio-temporal domains, this paper focuses on the shape matching of target trajectories. If some specific shapes frequently appear in historical trajectories, extracting these local shapes would be beneficial for analyzing the target motion templates and behavior modes. Trajectory segments with similar shapes may not have spatio-temporal correlation, and the shapes also have geometric transformation characteristics such as rotation, scaling and translation. Since the existing trajectory similarity analysis methods cannot be directly applied, an algorithm for extracting similar segments based on shape matching is proposed. First, a new shape descriptor based on signed barycenter distance (SBD) is established. It describes a trajectory as a one-dimensional shape feature sequence, which has the advantage of low computational complexity. Then, the distributed nearest neighbor search strategy is used in the particle swarm optimization (PSO) method, which aims to accelerate the retrieval of trajectory segments with similar shapes and improve the matching accuracy. Experiments on MPEG-7, handwritten character and maneuvering target simulation trajectory data sets show that compared with the existing typical shape descriptors, SBD shape descriptor has advantages in accuracy and noise insensitivity, and the improved PSO method can efficiently and accurately obtain the local shape matching results. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

Exploring a new environment is a huge challenge for the visually impaired. Hence there is a need for a system that is able to assist them safely during their journey. Here, in this paper, we propose a path planning with predetermined waypoints method using Particle Swarm Optimization (PSO) algorithm. This method computes shortest possible path given the predetermined waypoints from initial position to final position. By using predetermined waypoints which is a collection of coordinates along the pedestrian walkaway, a resulting accessible pedestrian path could be offered to the visually impaired. The system consists of a destination selection process and path planning process. For the destination selection process, a list of available places is given and the user will select the places that they want to visit from the list. The path planning process calculates path length between all nodes which includes predetermined waypoints, start point and final destination using Euclidean distance formula. The PSO algorithm will optimise possible shortest route by minimizing the total cost for path length. The simulation analyses of the proposed method have shown promising results of optimal route for different destinations. Results from this paper will be used further to explore the potential development of path guidance system for the visually impaired people by allowing them to travel independently in an unfamiliar environment. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of overcurrent relays (OCRs) coordination in the presence of DGs. OCRs are optimally set to work in a coordinated manner to isolate faults with minimal impacts on customers. The penetration of DGs into the power system changes the fault current levels seen by the OCRs. This can deteriorate the coordinated operation of OCRs. Operation time difference between backup and main relays can be below the standard limit or even the backup OCR can incorrectly work before the main OCR. Though resetting of OCRs is tedious especially in large systems, it cannot alone restore the original coordinated operation in the presence of DGs. The paper investigates the optimal utilization of fault current limiters (FCLs) to maintain the directional OCRs coordinated operation without any need to OCRs resetting irrespective of DGs status. It is required to maintain the OCRs coordination at minimum cost of prospective FCLs. Hence, the FCLs location and sizing problem is formulated as a constrained multi-objective optimization problem. Multi-objective particle swarm optimization is adopted for solving the optimization problem to determine the optimal locations and sizes of FCLs. The proposed algorithm is applied to meshed and radial power systems at different DGs arrangements using different types of FCLs. Moreover, the OCRs coordination problem is studied when the system includes both directional and non-directional OCRs. Comparative analysis of results is provided. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units’ contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units’ emission minimization. Accordingly, the ɛ-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ɛ-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Over the past decade, the particle swarm optimization (PSO) has been an effective algorithm for solving single and multi-object optimization problems. Recently, the chemical reaction optimization (CRO) algorithm is emerging as a new algorithm used to efficiently solve single-object optimization. In this paper, we present HP-CRO (hybrid of PSO and CRO) a new hybrid algorithm for multi-object optimization. This algorithm has features of CRO and PSO, HP-CRO creates new molecules (particles) not only used by CRO operations as found in CRO algorithm but also by mechanisms of PSO. The balancing of CRO and PSO operators shows that the method can be used to avoid premature convergence and explore more in the search space. This paper proposes a model with modified CRO operators and also adding new saving molecules into the external population to increase the diversity. The experimental results of the HP-CRO algorithm compared to some meta-heuristics algorithms such as FMOPSO, MOPSO, NSGAII and SPEA2 show that there is improved efficiency of the HP-CRO algorithm for solving multi-object optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, on-orbit constellations providing global communication and observation services have attracted widespread attention for economic and military interests. Thus, the design of satellite constellations has become a popular topic, which is mainly approached using different optimization methods. However, there are still some drawbacks to traditional methods, such as poor convergence and long computation time, which usually limit engineering applications. In this paper, a novel optimization method, the hybrid-resampling particle swarm optimization (HRPSO) algorithm, is proposed that provide higher efficiency for constellation design. The simulation results showed that the HRPSO algorithm is more efficient than the standard particle swarm optimization (PSO) algorithm and other improved resampling particle swarm optimization (RPSO) algorithms. Therefore, the HRPSO algorithm is expected to be a practical choice for constellation design. • Different types of sensors are considered, including complex cone and rectangular fields. • The attitude maneuver of the sensor is considered. • Different coverage performance indices are considered, including single- or double-fold coverage. • A novel and efficient HRPSO algorithm is proposed. [ABSTRACT FROM AUTHOR]