IEC 61850 communication-enabled cost optimisation for electric vehicle integration in microgrids

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

In this paper, we present a Hybrid Particle Swarm Optimization (HPSO) algorithm that combines the Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm (GA) method. The algorithm is used for investigating the plastic deformation behavior of titanium alloy (Ti-6Al-4V) in a multi-hole extrusion process. The simulation used rigid-plastic finite element (FE) DEFORMTM-3D software to obtain the minimum mandrel bias angle and exit tube bending angle. Results of the simulation indicate that these two angles were significantly less than 0.3 degrees, suggesting that metaheuristic algorithms based on HPSO and FE analysis could be used for general multi-hole extrusion processes.KeywordsMulti-hole extrusionParticle Swarm OptimizationFinite elementGenetic Algorithm

Novel Artificial Human Optimization Field Algorithms - The Beginning

D2D (Device-to-Device) communication is one of the promising technologies in the LTE (Long Term Evolution)-Advanced communication network. From system perspective, this technology can increase frequency efficiency of the whole macro cell. From user perspective, however, a D2D pair which shares the same frequency resource of CUE (Cellular User Equipment) can cause interference with the CUE in the downlink. Thus, reducing interference for the cellular users and allocating frequency resource for the D2D pairs are important in D2D communication. In this paper, two resource allocation schemes which are based on PSO (Particle Swarm Optimization) and hybrid PSO-GA (Genetic Algorithm) are proposed to maximize the system throughput by allowing up to two D2D pairs to share the same frequency resource with one CUE. Moreover, a scheme has been considered to mitigate the interference caused to CUEs. The simulation results show that service of CUEs and system performance can be effectively guaranteed by using the proposed schemes.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

This paper investigates the use of a Hybrid Energy Storage System (HESS) that contains a battery and fuel cell system with a microgrid (MG) to perform multiple functions such as supply-demand matching and energy arbitrage. The main objective of the proposed study is to obtain the optimal size of the MG system which minimizes the total costs and the total emissions. The problem is formulated as a nonlinear optimization problem with different constraints and solved using Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm. To validate the effectiveness of the HFPSO, the HFPSO is compared with the Particle Swarm Optimization (PSO) algorithm and the Firefly algorithm (FA). The result of the comparison proves the effectiveness of the HFPSO, and this is considered the main reason for using the HFPSO to solve the proposed difficult optimization problem. The problem is implemented using MATLAB software. The policy of the MG system is illustrated, and the results and the curves are discussed to validate this policy.

Adaptive equalization mitigates the distortions caused by radio channels. The least mean square (LMS) and the recursive least squares (RLS) algorithms are used for such purpose. Recently, particle swarm optimization (PSO) algorithms such as PSO using a linear time decreasing inertia weight (PSO-W) and the PSO using constant constriction factor (PSO-CCF) were shown to be very effective in handling systems having nonlinear behavior. However, these algorithms can be trapped in local minima. This paper presents a new PSO-based algorithm called the hybrid PSO (HPSO) that is capable to handle such problems. The HPSO includes the randomization of particles to improve the search capacity of the swarm, which in turn reduces the probability of being trapped in some local minima. It also adapts the inertia weight assignment to the particles. Extensive simulation results are conducted to confirm the consistency in the performance of the HPSO algorithm in different scenarios. The proposed HPSO secures the minimum steady-state error as compared to LMS and other PSO-based algorithms in both nonlinear and linear channels. Finally, the proposed HPSO algorithm shows a great improvements in Bit Error Rate and convergence rate.

A large project in a company is often divided to several subtasks, which would be assigned to different people with variant abilities to the same task. So whether the tasks are scheduled properly would determine the quality or the efficiency of team collaboration. A hybrid particle swarm optimization (PSO) algorithm is putted forward. Subtasks are disassembled from the project by using the task tree relations, and the tree structure is modeled into a task matrix. Moreover, task-time matrix is used to indicate the people abilities to complete the tasks. Then the hybrid algorithm was presented, in which simulated annealing method is added in particle swarm optimization to improve the capability of seeking the best allocating results. Finally, a simulation experiment is carried out by using the proposed algorithm, the comparing results show that the convergent velocity is fast and the optimizing ability is preferable.

Network-on-chip (NoC) has been proposed as a solution for the global communication challenges of system-on-chip (SoC) design in the nanoscale technologies. In this paper, a methodology is presented to develop an efficient routing algorithm for network-on-chip platforms that are specialized for an application or a set of concurrent applications. The proposed routing methodology, based on the hybrid particle swarm optimization (PSO) Algorithm, is applied on the 2D-mesh NoC platform to balance the link load. Experimental results show that this routing algorithm can efficiently assign deterministic, deadlock-free, minimal routing paths for traffic traces in a short period of time, and significantly guarantee the bandwidth requirement. In addition, the Hybrid PSO is combined with the operations of GA Algorithm, so that the algorithm achieves better performance.

This paper introduces a new algorithmic nature inspired approach that uses a hybridized Particle Swarm Optimization algorithm with a new neighborhood topology for successfully solving the Feature Selection Problem (FSP). The Feature Selection Problem is an interesting and important topic which is relevant for a variety of database applications. The proposed algorithm for the solution of the FSP, the Particle Swarm Optimization with Expanding Neighborhood Topology (PSOENT), combines a Particle Swarm Optimization (PSO) algorithm and the Variable Neighborhood Search (VNS) strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method by using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) the size of the neighborhood, it ends to a neighborhood that includes all the swarm, it manages to take advantage of the exploration capabilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method we use data sets of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.

A multi-compartment vehicle routing problem with time windows for urban distribution – A comparison study on particle swarm optimization algorithms

This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.

Now-a-days, submerged arc welding processes (SAW) are immensely being applied for joining the thick plates and surfacing application. However, the selection of optimal SAW process parameters is indeed an intricate task which aims to accomplish the desired quality of welded part at an economic way. Therefore, in the present paper, the research efforts are made on an implementation of efficient hybrid intelligent algorithms, i.e., hybrid particle swarm optimization and genetic algorithm (hybrid PSO-GA) for the optimization of SAW process parameters. The emphasis was given on different direct parameters such as voltage, wire feed rate, welding speed and nozzle to plate distance and indirect parameters such as flux condition and plate thickness, respectively. The parameters were chosen at two levels using fractional factorial design to study their effect on responses including flux consumption, metal deposition rate and heat input. Besides, the linear regression technique and analysis of variance were used for mathematical modeling of each response. Then, the direct effect and interaction effect on selected responses were investigated by 3D surface plots. At the end, the performance of hybrid PSO-GA is compared with general PSO and GA algorithms for indices including success rate, best solution, mean, computational time, standard deviation and mean absolute percentage error between. The overall results suggested that the hybrid PSO-GA is better option than other two algorithms, i.e., PSO and GA for obtaining the optimum SAW process parameters.

Purpose – The ultimate objective of the any e-Learning system is to meet the specific need of the online learners and provide them with various features to have efficacious learning experiences by understanding their complexities. Any e-Learning system could be much more improved by tracking students commitment and disengagement on that course, in turn, would allow system to have personalized involvements at appropriate times in order to re-engage learners. Motivations play a important role to get back the learners on the track could be done by analyzing of several attributes of the log files. This paper aims to analyze the multiple attributes which cause the learners to disengage from an online learning environment. Design/methodology/approach – For this improvisation, Web based learning system is researched using data mining techniques in education. There are various attributes characterized for the disengagement prediction using web log file analysis. Though, there have been several attempts to include motivating characteristics in e-Learning systems are adapted, presently influence on cognition is acknowledged mostly. Findings – Classification is one of the predictive data mining technique which makes prediction about values of data using known results found from different data sets. To find out the optimal solution for identifying disengaged learners in the online learning systems, Naive Bayesian (NB) classifier with Particle Swarm Optimization (PSO) algorithm is used which will classify the data set and then perform the independent analysis. Originality/value – The experimental results shows that the use of unrelated variables in the class attributes will reduce the accuracy and reliability of a any classification model. However, the hybrid PSO algorithm is clearly more apt to find minor subsets of attributes than the PSO with NB classifier. The NB classifier combined with hybrid PSO feature selection method proves to be the best feature selection capability without degrading the classification accuracy. It is further proved to be an effective method for mining large structural data in much less computation time.

The research and development of metaheuristic methods are critical issues in computer science. In the past decade, metaheuristic algorithms have been used in many engineering applications such as optimization of engineering problems, telecommunications, information security, and image processing. Many metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) are recently becoming very popular.There are many studies conducted in the literature on the comparison of PSO with other metaheuristic algorithms. In this chapter, various studies carried out between the years of 2010 and 2020 about the comparison of PSO with the other metaheuristic algorithms will be examined. The metaheuristic algorithms to be considered are simulated annealing (SA), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), tabu search (TS), harmony search (HS), firefly algorithm (FF), cuckoo search (CS), bat-inspired algorithm (BA), water wave optimization (WWO), clonal selection algorithm (CLONALG), chemical reaction optimization (CRO), sine cosine algorithm (SCA), glowworm swarm optimization (GSO), and grey wolf optimizer (GWO). This study aims to evaluate and analyze the covered papers according to several criteria such as (a) rates of studies according to publishing years, (b) the metaheuristic algorithms that are compared to PSO, (c) performance evaluation of compared algorithms, (d) the metaheuristic algorithms with their inspirational approaches and their initial proposed studies and years, (e) the field of subjects where the algorithms are applied in the reviewed studies, and (f) used databases in the examined studies.This study is a comprehensive literature review of the comparison of PSO with the most popular metaheuristic algorithms. The intention of this review is to be useful for researchers who want to conduct a survey on this area of the subject as this chapter will cover the essential and helpful analysis of the related research.