A Memetic Algorithm for the Generalized Traveling Salesman Problem

The generalized traveling salesman problem (GTSP) is an extension of the well-known traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The recent studies on this subject consider different variations of a memetic algorithm approach to the GTSP. The aim of this paper is to present a new memetic algorithm for GTSP with a powerful local search procedure. The experiments show that the proposed algorithm clearly outperforms all of the known heuristics with respect to both solution quality and running time. While the other memetic algorithms were designed only for the symmetric GTSP, our algorithm can solve both symmetric and asymmetric instances.Comment: 15 pages, to appear in Natural Computing, Springer, available online: http://www.springerlink.com/content/5v4568l492272865/?p=e1779dd02e4d4cbfa49d0d27b19b929f&pi=1

Optimal Dynamic Motion Sequence Generation for Multiple Harvesters

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Optimal Dynamic Motion Sequence Generation for Multiple Harvesters. Manuscript ATOE 07 001. Vol. IX. July, 2007

JPEG steganography with particle swarm optimization accelerated by AVX

Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.Web of Science328art. no. e544

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