Integrasi Kromosom Buatan Dinamis untuk Memecahkan Masalah Konvergensi Prematur pada Algoritma Genetika untuk Traveling Salesman Problem

Genetic Algorithm (GA) adalah metode adaptif yang digunakan untuk memecahkan masalah pencarian dan optimasi, diantaranya adalah Travelling Salesman Problem (TSP) yang merupakan persoalan optimasi, dimana rute terpendek merupakan solusi yang paling optimal. GA juga salah satu metode optimisasi global yang bekerja dengan baik dan efisien pada fungsi tujuan yang kompleks dalam hal nonlinear, tetapi GA mempunyai masalah yaitu konvergensi prematur. Untuk mengatasi masalah konvergensi prematur, maka pada penelitian ini diusulkan Dynamic Artificial Chromosomes (DAC) yang digunakan untuk mengkontrol keragaman populasi dan juga seleksi kromosom terbaik untuk memilih individu atau kromosom terbaik yang tujuannya untuk membuat keragaman pada populasi menjadi beragam dan keluar dari konvergensi prematur. Beberapa eksperimen dilakukan dengan menggunakan Genetic Algorithm Dynamic Artificial Chromosomes (GA-DAC), dimana threshold terbaik adalah 0.5, kemudian juga mendapatkan hasil perbaikan pada jarak terpendek yang dibandingkan dengan GA standar dengan dataset KroA100 sebesar 12.60%, KroA150 sebesar 13.92% dan KroA200 sebesar 12.92%. Untuk keragaman populasi mendapatkan hasil pada KroA100 sebesar 24.97%, KroA150 sebesar 50.84% dan KroA200 sebesar 49.08% dibandingkan dengan GA standar. Maka dapat disimpulkan bahwa GA-DAC bisa mendapatkan hasil lebih baik dibandingkan dengan GA standar, sehingga ini akan membuat GA bisa keluar dari konvergensi prematur

Solving travelling salesman problem using hybrid fluid genetic algorithm (HFGA)

Gezgin Satıcı Problemi (GSP), bir satıcının bütün şehirleri sadece bir defa ziyaret ederek başlangıç noktasına dönmesini sağlayan en kısa rotanın belirlendiği problemdir. GSP, araç rotalamadan baskılı devre kartı montajına kadar birçok problemin temelini oluşturur. Bu problem, optimizasyon alanında çalışan kişilerden büyük ilgi görmüştür, ancak özellikle büyük ölçekli veri kümeleri için çözülmesi zordur. Bu çalışmada, GSP’nin çözümü için Akışkan Genetik Algoritma, En Yakın Komşu ve 2-Opt sezgiselleri üzerine kurulu melez bir yöntem sunulmaktadır. Önerilen yöntemin performansı literatürde bulunan En Yakın Komşu, Genetik Algoritma, Tabu Arama, Karınca Kolonisi Optimizasyonu ve Ağaç Fizyolojisi Optimizasyon algoritmaları kullanılarak elde edilen çözüm değerleri ile kıyaslanmıştır. Önerilen yöntemin sonuçları çözüm süresi ve kalitesi bakımından üstünlük göstermektedir

Penentuan Centroid Awal Pada Algoritma K-Means Dengan Dynamic Artificial Chromosomes Genetic Algorithm Untuk Tuberculosis Dataset

Data merupakan hal penting diera sekarang begitu  juga dengan metode data mining yang dapat mengekstraksi data menghasilkan informasi. Klastering  1 dari 5 peran data mining yang berfungsi untuk mengelompokkan data berdasarkan tingkat kemiripan dan jarak minimum. Algoritma K-Means  termasuk algoritma yang populer banyak digunakan diberbagai bidang seperti bidang pendidikan, kesehatan, sosial, biologi, ilmu komputer. Seringkali metode K-Means dikombinasikan dengan metode optimasi seperti algoritma genetika untuk mengatasi permasalah pada K-Means yaitu sensitif dalam penentuan centroid awal .Namun metode algoritma genetika memiliki kekurangan yaitu mengalamai konvergen prematur sehingga hasil dari algorima genetika terjebak pada optimum lokal. Penelitian ini mengkombinasikan dynamic artificial cromosomes genetic algorithm dengan K-Means dalam menentukan nilai centroid awal pada k-means. Hasil eksperimen menunjukkan bahwa metode DAC GA + K-Means lebih unggul dibandingkan dengan K-Means dan GA + K-Means pada 2 dataset yang diuji dengan optimal nilai klaster sebanyak 2 dan 1 dataset sebanyak 3 klaster. Metode tersebut perolehan nilai DBI sebesar 0.138, 0.279 serta 0.382, nilai Sum Square Error sebesar 92.56, 332,39 dan 1280.68 serta nilai fitness yang tebentuk adalah 7.12, 3.57 dan 2.13

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

An QoS based multifaceted matchmaking framework for web services discovery

With the increasing demand, the web service has been the prominent technology for providing good solutions to the interoperability of different kind of systems. Web service supports mainly interoperability properties as it is the major usage of this promising technology. Although several technologies had been evolved before web service technology and this has more advantage of other technologies. This paper has concentrated mainly on the Multifaceted Matchmaking framework for Web Services Discovery using Quality of Services parameters. Traditionally web services have been discovered only with the functional properties like input, output, precondition and effect. Nowadays there is an increase in number of service providers leads to increase in the web services with same functionality. So user need to discover the best services so Quality of Service factors has been evolved. The traditional discovery supports only few quality parameters and so the discovery is easy in retrieval of services. As the parameter increases the matchmaking will be complex during service discovery. So in this proposed work, we have identified 21 QoS parameters which are suitable for service discovery. The information retrieval techniques are used to evaluate the results and results show that the proposed framework is better

A Protocol and Tool for Developing a Descriptive Behavioral Model

Fuzzy rules have been used to model complex human behavior in order to develop sophisticated industrial control systems. The use of fuzzy rules to create a behavioral model provides a quantitative basis for discussing the contribution of elements of the model to theories about the behavior. The application of a protocol and tool simplifies the development of a behavioral model from observational data. Extraction of a high level, linguistic behavioral model from the observational data is used to discover knowledge about the data. Tuning of the model is accomplished by parameter optimization through the adjustment of membership functions using the genetic fuzzy, self-adaptive system. A case study demonstrating the use of the protocol and tool is presented. In the study, a behavioral model is developed that integrates the analysis of the observational data with Social Network Analysis. The integrated behavioral model provides an effective platform for a quantitative analysis of the activities impacting behavior.  M.S

Heurística inspirada en el análisis sistémico del “Vecino más cercano”, para solucionar instancias simétricas TSP, empleando una base comparativa multicriterio

Esta tesis aporta evidencia que respalda al pensamiento sistémico como un pertinente campo de inspiración para el desarrollo de nuevos métodos de optimización heurística. Se delimita el alcance a la resolución del TSP simétrico como referente internacional de prueba que es. Se desarrollaron dos métodos: Sacrificio Cortoplacista Adaptativo 2-opt (SCA_2opt) y una versión mejorada (SCA_2_opt_r), los cuales son fruto del análisis sistémico de la regla vecino más cercano, encontrándole el arquetipo “Soluciones contraproducentes”. El SCA se basa en que el viajero renuncie en un momento dado a una ciudad inmediatamente cercana y se traslade hacia la segunda más cercana disponible, luego, el viajero continúa con la regla del vecino más cercano; cada que se realiza el SCA (búsqueda global) se efectúa una búsqueda local 2_opt. Las dos heurísticas resultan prometedoras en el balance entre eficacia y eficiencia en comparación con las heurísticas: vecino más cercano, Inserción más barata y 2-opt, y con 19 metaheurísticas en las categorías: Colonia de hormigas, Algoritmos genéticos, Enfriamiento simulado y Otras./Abstract. This thesis provides evidence that supports the relevant systems thinking as a field of inspiration for the development of new heuristic optimization methods. It delineates the scope of the resolution of the symmetric TSP as an international benchmark proves it. Two methods were developed: Adaptive short-term sacrifice 2-opt (SCA_2opt) and an enhanced version (SCA_2_opt_r), which are based on analysis systemic nearest neighbor rule, finding the archetypal "Solutions counterproductive ". The SCA is based on the traveler resign at any time a nearby town immediately and move to the second nearest available Of course, the traveler continues with the nearest neighbor rule, each SCA is performed (global search) is performed a local search 2_opt. The two heuristics are promising in the balance between effectiveness and efficiency compared to the heuristics: nearest neighbor, cheapest insertion and 2-opt, and with 19 metaheuristics in categories: ant colony, genetic algorithms, simulated annealing and Othe.Maestrí

Path planning algorithms for atmospheric science applications of autonomous aircraft systems

Among current techniques, used to assist the modelling of atmospheric processes, is an approach involving the balloon or aircraft launching of radiosondes, which travel along uncontrolled trajectories dependent on wind speed. Radiosondes are launched daily from numerous worldwide locations and the data collected is integral to numerical weather prediction.This thesis proposes an unmanned air system for atmospheric research, consisting of multiple, balloon-launched, autonomous gliders. The trajectories of the gliders are optimised for the uniform sampling of a volume of airspace and the efficient mapping of a particular physical or chemical measure. To accomplish this we have developed a series of algorithms for path planning, driven by the dual objectives of uncertainty andinformation gain.Algorithms for centralised, discrete path planning, a centralised, continuous planner and finally a decentralised, real-time, asynchronous planner are presented. The continuous heuristics search a look-up table of plausible manoeuvres generated by way of an offline flight dynamics model, ensuring that the optimised trajectories are flyable. Further to this, a greedy heuristic for path growth is introduced alongside a control for search coarseness, establishing a sliding control for the level of allowed global exploration, local exploitation and computational complexity. The algorithm is also integrated with a flight dynamics model, and communications and flight systems hardware, enabling software and hardware-in-the-loop simulations. The algorithm outperforms random search in two and three dimensions. We also assess the applicability of the unmanned air system in ‘real’ environments, accounting for the presence of complicated flow fields and boundaries. A case study based on the island South Georgia is presented and indicates good algorithm performance in strong, variable winds. We also examine the impact of co-operation within this multi-agent system of decentralised, unmanned gliders, investigating the threshold for communication range, which allows for optimal search whilst reducing both the cost of individual communication devices and the computational resources associated with the processing of data received by each aircraft. Reductions in communication radius are found to have a significant, negative impact upon the resulting efficiency of the system. To somewhat recover these losses, we utilise a sorting algorithm, determining information priority between any two aircraft in range. Furthermore, negotiation between aircraft is introduced, allowing aircraft to resolve any possible conflicts between selected paths, which helps to counteractany latency in the search heuristic