Time series predictive analysis based on hybridization of meta-heuristic algorithms

This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CSLSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities

An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation

This paper describes the use of an improved version of the Salp Swarm Algorithm, known as iSSA, to address Optimal Power Flow (OPF) issues in power system management. The iSSA is applied to OPF problems involving stochastic solar power generation, with the goal of optimizing control variables such as real power generation, voltage magnitude at generation buses, transformer tap settings, and reactive power compensation. The optimization aims to achieve three objectives: minimizing power loss, minimizing cost, and minimizing combined cost and emissions from power generation. The iSSA's performance was tested on a modified IEEE 30-bus system and compared to other recent algorithms, including SSA. The simulation results show that the iSSA outperformed all compared algorithms for all objective functions that have been derived in this study

Belajar Least Squares Support Vector Machines (LS-SVM) dalam masa seminggu

Bab 1: Pengenalan 1 Bab 2: Klassifikasi vs. Ramalan 3 Bab 3: Teori 5 Bab 4: Toolbox LS-SVM 15 Bab 5: Penyelesaian masalah klasifikasi menggunakan Toolbox LS-SVM 33 Bab 6: Penyelesaian masalah ramalan menggunakan Toolbox LS-SVM Bab 7: Penutup 79 Rujukan 8

Stock price predictive analysis : An application of hybrid barnacles mating optimizer with artificial neural network

Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next day’ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%

Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers

This paper proposes the implementation of various metaheuristic algorithms in solving the optimal power flow (OPF) with the presence of Flexible AC Transmission System (FACTS) devices in the power system. OPF is one of the well-known problems in power system operations and with the inclusion of the FACTS devices allocation problems into OPF will make the solution more complex. Thus, seven metaheuristic algorithms: Barnacles Mating Optimizer (BMO), Marine Predators Algorithm (MPA), Moth–Flame Optimization (MFO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching–Learning-Based Optimization (TLBO) and Heap-Based Optimizer (HBO) are used to solve two objective functions: power loss and cost minimizations. These algorithms are selected from the different metaheuristics classification groups, where the implementation of these algorithms into the said problems will be tested on the modified IEEE 14-bus system. From the simulation results, it is suggested that TLBO and HBO perform better compared to the rest of algorithms

Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines

The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe from the virus. Therefore, prediction of Covid19 confirmed cases is vital for the sake of proper prevention and precaution steps. In this study, a hybrid Barnacles Mating Optimizer with Least Square Support Vector Machines (BMO-LSSVM) is proposed for prediction of Covid19 confirmed cases. The employed data are the Covid19 cases in China which are defined in daily periodicity. The BMO was utilized to obtain optimal values of LSSVM hyper-parameters. Later, with the optimized values of the hyper-parameters, the prediction task will be executed by LSSVM. Through the experiments, the study recommends the superiority of BMO-LSSVM over the other identified hybrid algorithms

Solving optimal power flow problem with stochastic wind–solar–small hydro power using barnacles mating optimizer

Optimal Power Flow (OPF) is a complex and challenging problem in power system that includes non-convex and non-linear constrained optimization problems. Due to these features, solving the OPF problem is becoming a well-known area to be solved by researchers for the past decades especially involving with the proper optimizing of the control variables. This issue is vital to be solved in achieving the objectives while maintaining of the stability of the system. In this paper, recent metaheuristic algorithm namely Barnacles Mating Optimizer (BMO) will be used to solve three objective functions of OPF problem viz. (1) cost minimization of the power generation that consists of thermal and stochastic wind–solar–small hydro power generations, (2) power loss minimization, and (3) combined cost and emission minimization of mentioned power generations. To assess the performance of BMO into OPF problem, modified IEEE 30-bus and IEEE 57-bus systems that incorporate the stochastic wind–solar–small hydro power generators will be employed. Statistical studies are performed to show the feasible and effectiveness of BMO compared with other selected metaheuristic algorithms. Based on the obtained results, the BMO has shown the best results for all cases of simulation study. For example, the cost of power generations obtained by BMO for IEEE 30-bus and IEEE 57-bus systems are 789.1248 $/h and 5300.457$/hr, respectively which 1.45% and 2.2% cost saving per hour compared to the worst results obtained from compared algorithms. The results suggest that BMO performs better compared to the rest of algorithms and demonstrate as effective alternative for the OPF problem solution

Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers

Optimal power flow (OPF) is one of the complex problems in power system operation that includes multi-modal, large-scale, non-convex and non-linear constrained optimization problems. Due to these features, solving the OPF problem is becoming an active topic to be solved by power engineers and researchers. In this paper, recent metaheuristic algorithms namely Grasshopper Optimization Algorithm (GOA), Black Widow Optimization Algorithm, Grey Wolves Optimizer, Ant Lion Optimizer, Particles Swarm Optimization, Gravitational Search Algorithm, Moth-Flame Optimization and Barnacles Mating Optimizer (BMO) will be used to solve three objective functions of OPF problem viz. (1) cost minimization of the power generation that consists of thermal, stochastic wind and solar power generations, (2) power loss minimization, and (3) combined cost and emission minimization of power generations. To assess the performance of these selected metaheuristic algorithms on OPF, a modified IEEE 30-bus system that incorporate the stochastic wind and solar power generators will be used. Statistical studies are performed to identify the effectiveness of algorithms under consideration. Test results suggest that BMO performs better compared to the rest of algorithms and demonstrate that it can be effective alternative for the OPF problem solution

Optimal chiller loading solution for energy conservation using Barnacles Mating Optimizer algorithm

This paper proposes an application of evolutionary optimization algorithm, Barnacles Mating Optimizer (BMO) to solve the optimal chiller loading (OCL) problem for minimization of the power consumption in the multi chiller system. BMO mimics the mating behaviour of barnacles in nature. To generate new off-springs, the concept of Hardy–Weinberg principle is adopted for exploitation process and the sperm-cast process is treated as exploration behaviour of BMO. OCL on the other hand is the problem of finding the combination of partial load ratio of each chiller to obtain the minimum energy consumption in air-conditioning system. To demonstrate the effectiveness of proposed BMO, it is tested on three different chiller systems. The obtained results from BMO are compared with other well-known optimization algorithms in the literature. The obtained comparison results indicate that proposed BMO is effective to reach minimum energy consumption for solving the OCL problem