Optimizing the coefficients of the particle swarm optimization algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants

Environmental issues due to the emission of pollutants produced by fossil fuel power plants have recently become an important issue. In this study, the coefficients of particle swarm optimization (PSO) algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants were obtained. According to Clerk method, personal learning coefficient was equal to 1.4962, global learning coefficient was equal to 1.4962 and inertia coefficient was equal to 0.73. Also, the penalty coefficient according to the Co evolution particle swarm (CPSO) optimization algorithm was 15.8. As a result, optimization of coefficients by Taguchi method, it showed that the optimal value of personal learning coefficient is equal to 1.5, global learning coefficient is equal to 1.5, inertia coefficient is equal to 0.70 and penalty coefficient is equal to 15, in this case the amount emission of environmental pollutants were reduced by 6.5% compared to the coefficients determined by Clerk and 1.2% compared to the coefficients determined by the Co evolution particle swarm (CPSO) optimization algorithm

A Hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem

Distribution of work shifts and off days to nurses in a duty roster is a crucial task. In hospital wards, much effort is spent trying to produce workable and quality rosters for their nurses. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that could not be achieved in the manually generated rosters, which are still being practiced. Hence, this study focused on solving those issues arising in nurse rostering problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with the hill climbing is aiming at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model is developed with the goal of satisfying the hard constraints, while minimizing the violation of soft constraints in such a way that fulfill hospital’s rules and nurses’ preferences. The real data used for this highly constrained NRPs was obtained from a large Malaysian hospital. Specifically, three main phases were involved in developing the hybrid model, which are generating an initial roster, updating the roster through the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results show that at a larger value of pheromone, the chance of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that fulfilled all the four important criteria, which are coverage, quality, flexibility, and cost. Subsequently, the hybrid model is also beneficial to the hospital’s management whereby nurses can be scheduled with balanced distribution of shifts, which fulfill their preferences as well

Ant Colony Optimization with Hill Climbing for the Bandwidth Minimization Problem

In this work, the problem of reducing the bandwidth of sparse matrices by permuting rows and columns is addressed and solved using a hybrid ant system to generate high-quality renumbering which is refined by a hill climbing local search heuristic. Computational experiments compare the algorithm with the well-known GPS algorithm, as well as recently proposed methods. These show the new approach to be as good as current best algorithms. In addition, an algorithm to randomly generate matrices with known optimal bandwidth is developed and used to evaluate results. Comparisons show that the new algorithm was able to find either the optimal solution or a solution very close to the optimal for most instances. (C) 2005 Elsevier B. V. All rights reserved

Test Data Generation for Exposing Interference Bugs in Multi-Threaded Systems

RÉSUMÉ Tester les systèmes multi-thread est difficile en raison du comportement non-déterministe de l'environnement (ordonnanceurs, cache, interruptions) dans lequel ils s'exécutent. Comme ils ne peuvent contrôler l'environnement, les testeurs doivent recourir à des moyens indirects pour augmenter le nombre d'ordonnancements testés. Une autre source de non-déterminisme pour les systèmes multi-thread est l'accès à la mémoire partagée. Lors de leur exécution, les systèmes multi-thread peuvent générer des conditions de courses aux données ou d'interférence dues aux données partagées. La génération de jeux de test en utilisant des techniques basées sur les recherches locales a déjà fourni des solutions aux problèmes des tests de systèmes mono et multi-thread. Mais il n'y a pas encore eu de travaux abordant la question des bugs d'interférence dans les systèmes multi-thread en utilisant les recherches locales. Dans cette thèse, nous étudions la possibilité d'utiliser ces approches afin de maximiser la possibilité d'exposer des bugs d'interférence dans les systèmes multi-thread. Nous formulons notre hypothèse de recherche comme suit : les techniques basées sur les recherches locales peuvent être utilisées efficacement pour générer des jeux de test pour maximiser les conditions d’obtention de bugs d'interférence dans les systèmes multi-thread. Après étude de la littérature, nous avons découvert trois défis majeurs concernant l'utilisation des approches basées sur les recherches locales : C1 : Formuler le problème initial comme un problème de recherche locale, C2 : Développer une fonction de coût adaptée, et C3 : Trouver la meilleure recherche locale (la plus adaptée). Nous procédons d'abord à une étude préliminaire sur la façon dont ces défis peuvent être relevés dans les systèmes mono-thread. Nous pensons que nos résultats pourraient être ensuite applicables aux systèmes multi-thread. Pour notre première étude, nous abordons le problème de la génération des jeux de test pour lever des exceptions de type division par zéro dans un système mono-thread en utilisant des approches basées sur les recherches locales, tout en tenant compte de C1, C2 et C3. Nous constatons que les trois défis sont importants et peuvent être traitées, et que les approches basées sur les recherches locales sont nettement plus efficaces qu'une approche aléatoire lorsque le nombre de paramètres d'entrées croît. Nous traitons ensuite notre principal problème de génération de jeux de test afin de révéler des bugs d'interférence dans les systèmes multi-thread en utilisant des approches basées sur les recherches locales, tout en répondant aux trois mêmes défis. Nous avons constaté que même dans les systèmes multi-thread, il est important de traiter les trois défis et que les approches basées sur les recherches locales surpassent une approche aléatoire lorsque le nombre de paramètres d'entrée devient grand. Nous validons ainsi notre thèse. Cependant, d'autres études sont nécessaires afin de généraliser nos résultats.----------ABSTRACT Testing multi-threaded systems is difficult due to the non-deterministic behaviour of the environment (schedulers, cache, interrupts) in which the multi-threaded system runs. As one cannot control the environment, testers must resort to indirect means to increase the number of schedules tested. Another source of non-determinism in multi-threaded systems is the shared memory access. When executed, multi-threaded systems can experience one of many possible interleavings of memory accesses to shared data, resulting in data race or interference conditions being raised. Test data generation using search-based techniques has provided solutions to the problem of testing single and multi-threaded systems over the years. But to the best of our knowledge, there has been no work addressing the issue of interference bugs in multi-threaded systems using search-based approaches. In this thesis, we perform a feasibility study of using search-based approaches to maximize the possibility of exposing interference bug pattern in multi-threaded systems. We frame our thesis hypothesis as: Search-based techniques can be used effectively to generate test data to expose interference condition in multi-threaded systems. From the related work we found out three major challenges of using search-based approaches: C1: Formulating the original problem as a search problem, C2: Developing the right fitness function for the problem formulation, and C3: Finding the right (scalable) search-based approach using scalability analysis. Before studying multi-threaded systems, we perform a preliminary study on how these challenges can be addressed in single-threaded systems, as we feel that our findings might be applicable to the more complex multi-threaded systems. In our first study, we address the problem of generating test data for raising divide-by-zero exception in single-threaded systems using search-based approaches, while addressing C1, C2, and C3. We find that the three challenges are important and can be addressed. Further, we found that search-based approaches scale significantly better than random when the input search space grows from very small to huge. Based on the knowledge obtained from the study, we address our main problem of generating test data to expose interference bugs in multi-threaded systems using search-based approaches, while addressing the same challenges C1, C2, and C3. We found that even in multi-threaded systems, it is important to address the three challenges and that search-based approaches outperforms random when the input search-space becomes large. Thus we confirm our thesis. However, further studies are necessary to generalize

Um algoritmo de otimização por nuvem de partículas para resolução de problemas combinatórios

Resumo: O Particle Swarm Optimization (PSO) pertence a uma classe de algoritmos inspirados em comportamentos sociais naturais inteligentes, chamada Swarm Intelligence (SI). O algoritmo PSO tem sido aplicado com sucesso na resolução de problemas de otimização contínua, no entanto, o seu potencial em problemas discretos não foi suficientemente explorado. Trabalhos recentes têm proposto a implementação de PSO usando algoritmos de busca local e Path relinking com resultados promissores. Este trabalho tem como objetivo apresentar um algoritmo PSO como um meta-modelo que utiliza internamente busca local e Path relinking, mas diferentemente das abordagens anteriores, o algoritmo proposto mantém o conceito principal de PSO para a atualização da velocidade da partícula. O trabalho descreve o algoritmo proposto como uma plataforma geral para problemas combinatórios. Tal proposta é validada em duas implementações: uma aplicada ao Problema do Caixeiro Viajante e outra ao Problema da Mochila. As peculiaridades e uma série de experimentos de calibragem de ambos os algoritmos são relatados. Finalmente, a qualidade do algoritmo proposto é testada na comparação com outros PSO discretos da literatura recente e também com outro conhecido algoritmo de metaheurística: o Ant Colony Optimization (ACO). Os resultados são encorajadores e reforçam a idéia de que o algoritmo PSO também pode ser competitivo em espaço de busca discreto, assim como levam a crer que a utilização de métodos dependentes do problema pode ser uma excelente alternativa na aplicação de PSO a este tipo de problema