Permütasyon Akış Tipi Çizelgeleme Probleminin El Bombası Patlatma Metodu ile Çözümü

Üretimde kaynakların verimli kullanımı için işlerin en iyi şekilde çizelgelenmesi gerekmektedir. Gerçek hayatta çok sayıda uygulaması bulunan permütasyon akış tipi çizelgeleme problemi (PATÇP) yarım asırdan uzun süredir araştırmacıların ilgisini çekmektedir. El Bombası Patlatma Metodu (EBPM) Ahrari ve arkadaşları tarafından el bombalarının patlamalarından esinlenerek geliştirilmiş evrimsel bir algoritmadır. Bu çalışmada EBPM, permütasyon akış tipi çizelgeleme problemlerinin çözümü için uyarlanmıştır. Daha sonra metodu diğer metasezgisellerden ayıran özellik olan ajan bölgesi yarıçapının metot performansına etkisi araştırılmış ve metodun maksimum tamamlanma zamanı performans ölçütüne göre Taillard tarafından geliştirilmiş olan test problemleri üzerindeki performansları incelenmiştir. Sonuç olarak EBPM’nin makul sürelerde kabul edilebilir sonuçlara ulaşabildiği ve PATÇP’lerin çözümünde kullanılabileceği görülmüştür

Optimización PSO paralelizada para scheduling de flow-shop

El problema de scheduling de flow-shop (programación de la producción en una fábrica de flujo continuo) es de tipo NP-Hard, incluso para un número reducido de trabajos y de máquinas. Debido a su gran interés industrial, ha sido estudiado intensamente en las últimas décadas con el objeto de diseñar algoritmos que proporcionen soluciones de buena calidad en tiempos de cómputo aceptables para instancias de interés práctico. En este trabajo se presenta un algoritmo basado en optimización por enjambre de partículas (PSO) para el problema de scheduling de flow-shop. También se implementó una versión paralelizada que hace uso de placas gráficas NVIDIA utilizando la tecnología CUDA para acelerar las ejecuciones.Sociedad Argentina de Informática e Investigación Operativ

Optimización PSO paralelizada para scheduling de flow-shop

El problema de scheduling de flow-shop (programación de la producción en una fábrica de flujo continuo) es de tipo NP-Hard, incluso para un número reducido de trabajos y de máquinas. Debido a su gran interés industrial, ha sido estudiado intensamente en las últimas décadas con el objeto de diseñar algoritmos que proporcionen soluciones de buena calidad en tiempos de cómputo aceptables para instancias de interés práctico. En este trabajo se presenta un algoritmo basado en optimización por enjambre de partículas (PSO) para el problema de scheduling de flow-shop. También se implementó una versión paralelizada que hace uso de placas gráficas NVIDIA utilizando la tecnología CUDA para acelerar las ejecuciones.Sociedad Argentina de Informática e Investigación Operativ

Optimización PSO paralelizada para scheduling de flow-shop

El problema de scheduling de flow-shop (programación de la producción en una fábrica de flujo continuo) es de tipo NP-Hard, incluso para un número reducido de trabajos y de máquinas. Debido a su gran interés industrial, ha sido estudiado intensamente en las últimas décadas con el objeto de diseñar algoritmos que proporcionen soluciones de buena calidad en tiempos de cómputo aceptables para instancias de interés práctico. En este trabajo se presenta un algoritmo basado en optimización por enjambre de partículas (PSO) para el problema de scheduling de flow-shop. También se implementó una versión paralelizada que hace uso de placas gráficas NVIDIA utilizando la tecnología CUDA para acelerar las ejecuciones.Sociedad Argentina de Informática e Investigación Operativ

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

PSO-based algorithm applied to quadcopter micro air vehicle controller design

[[abstract]]Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO)-based algorithm and the evolutionary programming (EP) algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models.[[notice]]補正完

A simple and effective approach for tackling the permutation flow shop scheduling problem

In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIEGA and IEGA are validated on three common benchmarks and compared with a number of well-known robust evolutionary and meta-heuristic algorithms to check their efficacy. The experimental results show that HIEGA and IEGA are competitive with others for the datasets incorporated in the comparison, such as Carlier, Reeves, and Heller.</p