Enhancing FastSLAM 2.0 performance using a DE Algorithm with Multi-mutation Strategies

FastSLAM 2.0 is considered one of the popular approaches that utilizes a Rao-Blackwellized particle filter for solving simultaneous localization and mapping (SLAM) problems. It is computationally efficient, robust and can be used to handle large and complex environments. However, the conventional FastSLAM 2.0 algorithm is known to degenerate over time in terms of accuracy because of the particle depletion problem that arises in the resampling phase. In this work, we introduce an enhanced variant of the FastSLAM 2.0 algorithm based on an enhanced differential evolution (DE) algorithm with multi-mutation strategies to improve its performance and reduce the effect of the particle depletion problem. The Enhanced DE algorithm is used to optimize the particle weights and conserve diversity among particles. A comparison has been made with other two common algorithms to evaluate the performance of the proposed algorithm in estimating the robot and landmarks positions for a SLAM problem. Results are accomplished in terms of accuracy represented by the positioning errors of robot and landmark positions as well as their root mean square errors. All results show that the proposed algorithm is more accurate than the other compared algorithms in estimating the robot and landmark positions for all the considered cases. It can reduce the effect of the particle depletion problem and improve the performance of the FastSLAM 2.0 algorithm in solving SLAM problem

Optimisasi Setting Directional Overcurrent Relay Dengan Menggunakan Hybrid Algoritma (DE – PSO)

To prevent interference and damage to equipment in the electric power system, the role of a protection system is needed. An unreliable protection system has the impact of economic losses, this is the basis for engineers to conduct research on the optimization of protection coordination in the electric power system. This study aims to ensure that the protection system can run properly in the event of a short circuit fault in the system. In addition, this research is to facilitate engineers in the calculation of the Directional Overcurrent Relay (DOCR) coordination study. In contrast to previous studies, this research implements a hybrid algorithm method on a real electrical system by considering the generator operating scheme. Due to the generator operating conditions that are always changing, it affect DOCR working time. To overcome these problems, it is necessary to set an optimal DOCR using the meta-heuristic method. This is the basis for researchers to conduct research on optimization of protection coordination settings. Each meta-heuristic method such as Differential Evolution Algorithm and Particle Swarm Optimization has its own advantages and disadvantages in the process of searching the optimization. Therefore, this research implements a hybrid strategy of Differential Evolution Algorithm – Particle Swarm Optimization (DE-PSO) to get the DOCR Time Dial Setting (TDS) value. From the results of the study, it can be concluded that using a hybrid algorithm is 83% more efficient than the non-hybrid method. Keywords: Directional Overcurrent Relay, Differential Evolution Algorithm, Particle Swarm OptimizationUntuk mencegah adanya gangguan dan kerusakan peralatan pada sistem tenaga listrik diperlukan peranan sistem proteksi. Sistem proteksi yang tidak handal memiliki dampak kerugian ekonomi, hal inilah menjadi dasar para engineer untuk melakukan penelitian optimasi koordinasi proteksi pada sistem tenaga listrik. Penelitian ini bertujuan untuk memastikan sistem proteksi dapat berjalan dengan baik jika terjadinya gangguan hubung singkat pada sistem. Selain itu, penelitian ini untuk mempermudah engineer dalam perhitungan studi koordinasi Directional Overcurrent Relay (DOCR). Berbeda dengan penelitian sebelumnya, dalam penelitian ini mengimplementasikan metode hybrid algoritma pada sistem kelistrikan real dengan mempertimbangkan skema operasi generator. Dengan adanya kondisi operasi generator yang selalu berubah-ubah, kondisi tersebut mempengaruhi waktu kerja DOCR. Untuk mengatasi permasalahan tersebut, perlu melakukan setting DOCR yang optimal dengan menggunakan metode metaheuristik. Hal tersebut menjadi dasar peneliti untuk melakukan penelitian optimasi setting koordinasi proteksi. Setiap metode metaheuristik seperti Differential Evolution Algorithm dan Particle Swarm Optimization memiliki kekurangan dan kelebihan masing-masing dalam proses pencarian optimisasi. Oleh karena itu, penelitian ini mengimplementasikan strategi hybrid algoritma Differential Evolution Algorithm – Particle Swarm Optimization (DE-PSO) untuk mendapatkan nilai Time Dial Setting (TDS) DOCR. Dari hasil penelitian dapat disimpulkan dengan menggunakan hybrid algoritma lebih efisien 83% dari metode non-hybrid. Kata kunci: Directional Overcurrent Relay, Differential Evolution Algorithm, Particle Swarm Optimizatio

Determination of an effective implementation of the differential evolution method to power shortage minimization

Constant development of electric power systems leads to their constant enlargement and complication; new ways of their control appear. In this regard, the existing models and software for adequacy assessment may work defective and ineffectively from the point of view of the adequacy of the obtained results, as well as the speed and accuracy of calculations. The key role in adequacy assessment of electric power systems (EPS) are played by optimization methods that allow to correctly determine the minimum of power shortage that occurs in various states of the EPS. A review of modern computational systems for adequacy assessment showed that the general concept of mathematical models is the same and can be described within the framework of the flow distribution problem. Despite this, each mathematical model is unique in its own way and requires an individual approach to its optimization. The purpose of this work is to analyze the efficiency of calculations in terms of accuracy and speed of various versions of the differential evolution (DE) method for the specified mathematical models within the framework of adequacy assessment of EPS. To achieve this goal, we solved several problems: two mathematical models were identified - a nonlinear model for minimizing the power shortage with the quadratic losses in flows and its modification with the controlled sections; differential evolution methods, including standard DE, composite DE, JDE, chaotic DE, adaptive DE; mutation strategies: DE/rand/1, DE/best/1, DE/rand/2, DE/best/2, DE/rand/3, DE/best/3, DE/current-to-rand/1, and DE/current-to-best/1. In this paper we tested the effectiveness of differential evolution methods, with different mutation strategies and different scales of EPSs. The experimental part was carried out using a software that was independent development by authors using C++. This complex includes the implementation of mathematical models and methods. The methods were tested on two systems with different numbers of adequacy zones, including those with three and seven adequacy zones. According to the research results, self-adaptive methods of DE are of the greatest interest for the further use and development of methods for this problem, due to automatic adjustment of the method parameters for each of the considered models and systems

Multi-objective power quality optimization of smart grid based on improved differential evolution

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Data for: Comparison of Mutation Strategies in Differential Evolution -- a Probabilistic Perspective

This is supplementary material to the article ``Comparison of Mutation Strategies in Differential Evolution -- a Probabilistic Perspective''. It provides more detailed benchmark results than those reported in the paper. The design of the experiment, which serves also as the ``table of contents'', is provided in Table 1.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Comparison of Mutation Strategies in Differential Evolution -- a Probabilistic Perspective

This is supplementary material to the article ``Comparison of Mutation Strategies in Differential Evolution -- a Probabilistic Perspective''. It provides more detailed benchmark results than those reported in the paper. The design of the experiment, which serves also as the ``table of contents'', is provided in Table 1