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" PSS (Power system stabilizer) telah digunakan secara luas untuk memperbaiki stabilitas sistem tenaga listrik modern. Dalam makalah ini diusulkan perancangan sistematik PSS dengan Particle Swarm Optimization (PSO) sebagai metode optimasi penalaan parameter PSS. Penalaan parameter PSS dilakukan untuk mendapatkan sistem tenaga listrik yang stabil dan teredam secara optimal. Kriteria optimal yang digunakan dalam proses penalaan parameter adalah indeks performansi Integral of Time multiplied by Absolute Error (ITAE). Performansi dariPSS ini diujikan pada sistem tenaga listrik mesin tunggal dibawah gangguan kecil, kondisi beban dan parameter tertentu. Hasil analisa nilaieigen dan simulasi menunjukkan bahwa osilasi sistem tenaga listrik dapat teredam secara optimal melalui penalaan PSS berbasis PSO ini. Hasil simulasi juga menunjukkan bahwa performansi dinamik PSS berbasis PSO lebih baik dibandingkan PSS yang ditala secara konvensional.

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AbstractPower system stabilizer (PSS) have been extensively used in modern power system for enhancing stability of the system. This paper presents a new systematic approach for the design of power systemstabilizer using PSO (Particle Swarm Optimization). The proposed approach employs PSO search for optimal setting of PSS parameters. The optimal criteria of the Integral of Time multiplied by AbsoluteError (ITAE) is used to search optimal setting. The performance of the proposed PSS under small disturbances, loading conditions and system parameters is tested. The eigenvalue analysis and simulationresults show the effectiveness of the PSO based PSS to damp out the system oscillations. It is found that the dynamic performance with the PSO based PSS shows improved results, over conventionally tunedPSS."

"ABSTRAKJurnal ini menyajikan perancangan sistem mekanikal dan elektrikal, proses manufaktur dan perakitan, dan proses pengukuran dan parameterisasi mobile microrobot untuk sistem vision control dan sistem kolaborasi swarm intelligence. Robot terdiri dari dua roda dimana rodanya saling bergerak secara independen dan disertai dengan sistem komunikasi nirkabel yaitu dengan menggunakan wifi. Sistem kendali pada robot menggunakan sistem vision control dimana kamera menangkap gambar dari robot yang kemudian diproses dengan menggunakan matlab, rangka robot harus dirancang dengan baik agar sistem vision control dapat mengenali robot. Data perintah yang dihasilkan oleh matlab kemudian disimpan pada web server yang selanjutnya robot membaca data dari web server yang kemudian diubah oleh microcontroller menjadi sinyal yang akan mengaktifkan aktuator dan sistem koordinasi antar robot menggunakan sistem swarm intelligence untuk menyelesaikan sebuah permasalahan.

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ABSTRACTThis paper presents the mechanical and electrical design, manufacturing and assembly process, and mobile microrobot measurement and parameterizing process for vision control system and swarm intelligence collaboration system. The robots consist of two wheels in which the wheels can move independently and the robots is equipped with wireless communication system by using a Wi Fi. The robots use vision control system in which the camera takes the robot images and it rsquo s processed using matlab, the chassis must be designed well so that vision control system can recognize the robot. The command data that generated by matlab is stored at web server, the data that stored at web server is read by microcontroller and changed into a signal to activate the actuators and the coordination system between robots using swarm intelligence system to solve a problem."

"This book constitutes the proceedings of the 8th International Conference on Swarm Intelligence, held in Brussels, Belgium, in September 2012. This volume contains 15 full papers, 20 short papers, and 7 extended abstracts carefully selected out of 81 submissions. The papers cover various topics of swarm intelligence."

"This book and its companion volume, LNCS vols. 7331 and 7332, constitute the Proceedings of the Third International conference on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 full papers presented were carefully reviewed and selected from 247 submissions. The papers are organized in 27 cohesive sections covering all major topics of swarm intelligence research and developments."

"Perkembangan platform transportasi online mengubah perilaku masyarakat, khususnya dalam layanan pesan-antar makanan online. Tingginya penggunaan layanan ini menimbulkan masalah terkait tingginya jumlah kurir plaform transportasi online yang harus beroperasi untuk memenuhi semua permintaan layanan. Salah satu solusi untuk mengatasi hal tersebut yaitu dengan menggabungkan dua permintaan pesanan (order) dari pelanggan yang lokasinya berdekatan, cukup dilayani oleh satu kurir dengan memperhatikan beberapa kendala sehingga tetap menguntungkan semua pihak. Pelanggan dalam penelitian ini mengacu pada merchant dan customer. Penggabungan dua pelanggan yang dilayani oleh satu kurir di sini disebut sebagai fitur double orders. Penelitian ini melakukan optimasi rute pesan-antar makanan pada layanan transportasi online yang mengimplempentasikan penggunaan fitur double orders dengan mempertimbangkan kepuasan pelanggan dan bertujuan meminimumkan biaya operasional. Kepuasan pelanggan yang dimaksud terkait dengan rentang waktu (time windows) pengambilan dan pengantaran makanan agar kualitasnya tetap terjaga. Kurir yang melewati rentang waktu yang telah ditetapkan akan dikenakan biaya penalti. Masalah optimasi rute layanan pesan-antar makanan ini dimodelkan dalam bentuk Vehicle Routing Problem Pickup Delivery with Time Windows (VRPPDTW), untuk penyelesaiannya digunakan metode Particle Swarm Optimization (PSO). Metode PSO adalah metode metaheuristik yang terinspirasi dari perilaku kawanan organisme (swarm) yang menyimpan informasi mengenai posisi (solusi) terbaik. Pada penelitian ini digunakan data yang terdiri dari 50 permintaan layanan pesan-antar makanan. Hasil yang diperoleh dari penerapan metode PSO untuk mengoptimasi rute kurir layanan pesan-antar dengan fitur double orders tersebut mampu mengurangi jumlah kurir yang beroperasi hingga 50% dan menghemat total biaya operasional yang harus dikeluarkan hingga 36.65%.

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The development of online transportation platforms has changed public behavior, especially in online food takeaway-delivery services. The high demand for these services has led to issues related to the large number of online transportation platforms drivers needed in fulfilling all service requests. One solution to this problem is by combining two orders from clients located nearby which can be served by a single driver while considering several constraints to ensure it benefits all parties involved. In this study, clients refer to both merchant and customer. The process of combining two clients served by a driver is referred to as the double orders feature. This study focuses on optimizing food takeaway-delivery routes in online transportation services by implementing the double orders feature, considering clients satisfaction and aiming to minimize operational costs. Clients’ satisfaction here relates to the time windows for picking up and delivering food to ensure its quality is maintained. Drivers who exceed the designated time windows will incur penalty costs. The problem of optimizing food takeaway-delivery service routes is modeled as a Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPPDTW), and it is solved using the Particle Swarm Optimization (PSO) method. PSO is a metaheuristic method inspired by the behavior of swarms of organisms, which store information about the best positions (solutions). In this study, data consisting of 50 food takeaway-delivery requests is used. The results obtained from applying the PSO method to optimize drivers routes with the double orders feature show that it can reduce the number of operating drivers by up to 50% and save total operational costs by 36.65%."

"Kompleksitas menuntut para peneliti untuk membuat kecerdasan buatan. Skripsi ini membahas tentang implementasi algoritma pencarian rute Swarm Intelligece pada sistem kontrol visual berbasis MATLAB sebagai kontrol Mobile Microrobot yang menggunakan modul mikro kontroler ESP8266. Robot bergerak dengan menerima perintah dari sistem kontrol menggunakan koneksi nirkabel WiFi. Algoritma pencarian rute yang digunakan merupakan sifat dari kecerdasan kerumunan semut ant colony , yang meliputi: penemuan rute route discovery , pemeliharaan rute route maintenance . Hasil akhir dari penelitian ini adalah sistem kontrol visual telah di-implementasikan algoritma pencarian rute swarm intelligence dari sifat yang dimiliki ant colony.

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Complexity demanding to the researchers to make artificial intelligence. The focus of this study is about implementing swarm intelligence for routing algorithm on the visual servoing control system MATLAB based, to control mobile microrobot with ESP8266 micro controller module. The robot moves by receiving task from a control system using a WiFi wireless connection. Routing Algorithm used is the nature intelligence from ant colony, which include route discovery, route maintenance, The end result of this research is visual control system has been implemented with swarm intelligence routing algorithm from the nature of ant colony. "

"This book focuses on a different, complementary inspiration, namely physics. The editors have introduced the term 'physicomimetics' to refer to physics-based swarm approaches, which offer two advantages. The editors consolidated over a decade of work on swarm intelligence and swarm robotics, organizing the book into 19 chapters as follows. Part I introduces the concept of swarms and offers the reader a physics tutorial. Part II deals with applications of physicomimetics, in order of increased complexity. Part III examines the hardware requirements of the presented algorithms and demonstrates real robot implementations. Part IV demonstrates how the theory can be used to design swarms from first principles and provides a novel algorithm that handles changing environments. Part V shows that physicomimetics can be used for function optimization, moving the reader from issues of swarm robotics to swarm intelligence. The text is supported with a downloadable package containing simulation code and videos of working robots. "

"The 0-1 multidimensional knapsack problem (MKP) has been proven it belongs to difficult NP-har combinatorial optimization problems. There are various search algorithms based on population concept to solv these problems. the particle swarm optimization (PSO) technique is adapted in our stucy, which proposes a novel PSO algorithm, namely, the binary PSO based on surrogate information with proportional acceleration coefficients (BPSOSIPAC). the proposed algorithm was tasted on 135 benchmark problems from the OR-Library to validate and demonstrate the efficiency in solving multidimensional knapsack problems. The result were then compared with those in the other nine existing PSO algorithms. The simulation and evaluation result showed that the proposed algorithm, BPSOSIPAC, is superior to the of successful runs, average eror (AE) , mean absolute deviation, mean absolute percentage error, last error, standard deviation, best profit, mean profit, worst profit, AE of the best profit (%), AE of the mean profit deviaton. "

"Nowadays, the pursuance of sustainability obligates manufacturers to redesign products in order to reduce negative environmental impacts. However, only a few studies have simultaneously considered environmental sustainability and assemblability. To bridge this research gap, this study aimed to develop a redesign method based on modular product architecture. This method manages to support a sustainable product considering its materials, assembly sequence and line balance at initial design phase. This method begins with a current product analysis based on economic and environmental performances (i.e., total cost and CO2 emissions). Additionally, new materials and assembly methods are incorporated into redesigning a more sustainable product without compromising production performance. To ensure assemblability, the line balance of 60% is served as one of the constraints. This study applies the particle swarm optimization algorithm to calculate an optimal module organization along with assembly methods and assembly sequences. An air purifier case study is presented to demonstrate the benefits of the proposed method. As a result, the redesigned product can be more easily maintained during product usage and be recycled at the end of product life."

"The volume LNCS 7269 constitutes the refereed proceedings of the International Symposium on Swarm Intelligence and Differential Evolution, SIDE 2012, held in Zakopane, Poland, in April/May 2012 in conjunction with the 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2012 (proceedings published as two-volume set LNAI 7267 and 7268). The 212 revised full papers presented were carefully reviewed and selected from 483 submissions. The volume is divided into two topical parts: proceedings of the 2012 symposium on swarm intelligence and differential evolution and on evolutionary algorithms and their applications."