Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem

Particle Swarm Optimization is an evolutionary method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The evolution is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of the inertia and the attraction of the members who lead the swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels

Penerapan Metode Hybrid Fuzzy C-Means Dan Particle Swarm Optimization (FCM - PSO) E Untuk Segmentasi Citra Geografis

Beberapa lapisan dari Sistem Informasi Geografis (SIG) bisa dibedakan oleh mata telanjang dari sebuah citra satelit namun pasti akan melelahkan jika mengamati citra begitu banyak. Penelitian ini dilakukan untuk melakukan otomasi pengamatan dengan metode segmentasi. Metode segmentasi yang diusulkan adalah Hybrid Fuzzy C-Means – Particle Swarm Optimization (FCM-PSO). Hasil penelitian menunjukkan FCM-PSO lebih unggul dari FCM biasa sekalipun dengan kelemahan waktu eksekusi yang lebih panjang.Kata Kunci—FCM, PSO, Segmentasi, SI

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Gezgin Satıcı Problemi (GSP), başlangıç ve bitiş şehirleri aynı olan ve her şehrin sadece bir kez ziyaret edildiği minimum mesafeli turu bulma problemidir. Şehir sayısı arttıkça, kesin yöntemler ile kabul edilebilir sürelerde bir optimum çözüm bulunması zordur. Bu nedenle, son elli yılda GSP’nin çözümü için doğadan ve biyolojiden esinlenen birçok meta-sezgisel yöntem geliştirilmiştir. Bu çalışmada, toprak altındaki bireysel tünel sistemlerinde yaşayan kör farelerin toprak altındaki engelleri geçme stratejisinden esinlenilerek GSP’nin çözümü için yeni bir meta-sezgisel tasarlanmıştır. Geliştirilen yönteme Kör Fare Algoritması adı verilmiştir. Bu yeni sezgisel ile farklı boyutlardaki simetrik test veri setleri için deneyler yapılmış ve sonuçları bilinen en iyi sonuçlar ile kıyaslanmıştır. Önerilen meta-sezgisel henüz literatürdeki diğer algoritmalarla yarışabilecek düzeyde olmamasına rağmen, başlangıç test çözümlerinin umut verici olduğu söylenebilir.Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

DISCRETE PARTICLE SWARM OPTIMIZATION FOR THE ORIENTEERING PROBLEM

Discrete particle swarm optimization (DPSO) is gaining popularity in the area of combinatorial optimization in the recent past due to its simplicity in coding and consistency in performance.  A DPSO algorithm has been developed for orienteering problem (OP) which has been shown to have many practical applications.  It uses reduced variable neighborhood search as a local search tool.  The DPSO algorithm was compared with ten heuristic models from the literature using benchmark problems.  The results show that the DPSO algorithm is a robust algorithm that can optimally solve the well known OP test problems

Using 2-Opt based evolution strategy for travelling salesman problem

Harmony search algorithm that matches the (µ+ 1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results

Attack on the Simple Substitution Ciphers Using Particle Swarm Optimization

This paper considers a new approach to cryptanalysis based onsimulation of behavior of flocks of birds and schools of fish called ParticleSwarm Optimization (PSO). It is shown that such algorithm could be usedto break the key for a simple substitution cipher. This paper presents aproposed 2-opt PSO algorithm to enhance the efficiency of PSO algorithmon attacking simple substitution ciphers