Novel analysis–forecast system based on multi-objective optimization for air quality index

© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process

An Optimal Routing Protocol Using a Multiverse Optimizer Algorithm for Wireless Mesh Network

Wireless networks, particularly Wireless Mesh Networks (WMNs), are undergoing a significant change as a result of wireless technology advancements and the Internet's rapid expansion. Mesh routers, which have limited mobility and serve as the foundation of WMN, are made up of mesh clients and form the core of WMNs. Mesh clients can with mesh routers to create a client mesh network. Mesh clients can be either stationary or mobile. To properly utilise the network resources of WMNs, a topology must be designed that provides the best client coverage and network connectivity. Finding the ideal answer to the WMN mesh router placement dilemma will resolve this issue MRP-WMN. Since the MRP-WMN is known to be NP-hard, approximation methods are frequently used to solve it. This is another reason we are carrying out this task. Using the Multi-Verse Optimizer algorithm, we provide a quick technique for resolving the MRP-WMN (MVO). It is also proposed to create a new objective function for the MRP-WMN that accounts for the connected client ratio and connected router ratio, two crucial performance indicators. The connected client ratio rises by an average of 16.1%, 12.5%, and 6.9% according to experiment data, when the MVO method is employed to solve the MRP-WMN problem, the path loss falls by 1.3, 0.9, and 0.6 dB when compared to the Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), correspondingly

Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works

Multi-waypoint-based path planning for free-floating space robots

This paper studies the multi-waypoint-based path planning problem (MWPP) for redundant space robots. The end-effector of a space robot should visit a set of predefined waypoints with optimal distance, and the free-floating base should suffer minimum attitude disturbances from the manipulator during manoeuver. The MWPP is decomposed into two sub-problems: the problem of optimal waypoint-sequence and the problem of optimal joint-movements. First, the Hybrid Self-adaptive Particle Swarm Optimization algorithm is proposed for optimal waypoint-sequence. Second, an Improved Particle Swarm Optimization algorithm, combined with direct kinematics of the space robot, is proposed for optimal jointmovements. Finally, simulations are presented to validate the approach, including comparisons with other approaches

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.Метою статті є визначення оптимального розміру та розташування фотоелектричних систем розподіленої генерації та систем розподільних статичних синхронних компенсаторів (DSTATCOM) для мінімізації втрат активної потужності у розподільній мережі та покращення профілю напруги. Методологія, що використовується в цій статті, починається з детального обговорення різних алгоритмів прискорення, що використовуються в оптимізації рою частинок (PSO), та їх варіацій на кожній ітерації. Згодом було перевірено низку алгоритмів PSO, кожен з яких включає різні варіанти коефіцієнтів прискорення, для вирішення проблеми втрат активної потужності та покращення напруги. Результати моделювання, одержані на радіальній розподільній мережі шини стандарту IEEE-33, підтверджують ефективність коефіцієнтів прискорення PSO; він був оцінений та порівняний з іншими описаними в літературі методами покращення профілю напруги та зниження активної потужності. Оригінальність. Полягає у визначенні найбільш ефективного методу серед різних коефіцієнтів прискорення PSO з погляду мінімізації втрат активної потужності та покращення профілю напруги в енергосистемі. Крім того, демонструє перевагу обраного методу над іншими для досягнення значного підвищення ефективності енергосистеми. Практична цінність цього дослідження полягає у його здатності надати практичні рішення для оптимального розміщення та визначення розмірів розподіленої генерації та DSTATCOM. Запропонований метод оптимізації дає відчутні переваги для експлуатації та керування енергосистемою. Ці результати мають практичне значення для фахівців із планування енергосистем, операторів та розробників політики керування, дозволяючи їм приймати обґрунтовані рішення щодо ефективної інтеграції технологій розподіленої генерації та технологій DSTATCOM