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About 10,000 papers for “Optimization and Search Problems”

Resolution Search and Discrete Optimization Problems

Marius Posta, MICHELON, Philippe, FERLAND, Jacques · theses.fr (ABES) · 2012

Les problèmes d optimisation discrète sont pour beaucoup difficiles à résoudre, depar leur nature combinatoire. Citons par exemple les problèmes de programmationlinéaire en nombres entiers. Une approche couramment employée pour les résoudreexactement est l approche de Séparation et Évaluation Progressive. Une approchedifférente appelée Resolution Search a été proposée par Chvátal en 1997 pourrésoudre exactement des problèmes d optimisation à variables 0-1, mais elle restemal connue et n a été que peu appliquée depuis.Cette thèse tente de remédier à cela, avec un succès partiel. Une première contributionconsiste en la généralisation de Resolution Search à tout problème d optimisationdiscrète, tout en introduisant de nouveaux concepts et définitions. Ensuite,afin de confirmer l intérêt de cette approche, nous avons essayé de l appliquer enpratique pour résoudre efficacement des problèmes bien connus. Bien que notrerecherche n ait pas abouti sur ce point, elle nous a amené à de nouvelles méthodespour résoudre exactement les problèmes d affectation généralisée et de localisationsimple. Après avoir présenté ces méthodes, la thèse conclut avec un bilan et desperspectives sur l application pratique de Resolution Search.

Cited by 0Open access

GENET and Tabu Search for Combinatorial Optimization Problems

Dr Boyce, C. H. D. Dimitropoulos, G. Vom Scheidt et al. · 1995

. Constraint satisfaction problems are combinatorial optimization problems which involve finding an assignment to a set of variables that is consistent with a set of constraints. In this paper the use of two local search techniques, namely GENET and Tabu Search for constraint satisfaction optimization problems and partial constraint satisfaction problems is investigated. These methods are compared by application to a difficult partial constraint satisfaction optimization problem, namely the Radio Links Frequency Assignment Problem (RLFAP). 1 Introduction A constraint satisfaction problem consists of finding an assignment to a set of variables that is consistent with a set of constraints. In this paper we assume that the number of variables is finite and the respective domains are also finite and discrete. Constraints restrict the configurations which may be assigned to subsets of the variables. There are many real-world problems that can be described within this formalism, for example...

Cited by 0

Boosted Reptile Search Algorithm for Engineering and Optimization Problems

Mohamed Abd Elaziz, Samia Allaoua Chelloug, Mai Alduailij et al. · Applied Sciences · 2023

Recently, various metaheuristic (MH) optimization algorithms have been presented and applied to solve complex engineering and optimization problems. One main category of MH algorithms is the naturally inspired swarm intelligence (SI) algorithms. SI methods have shown great performance on different problems. However, individual MH and SI methods face some shortcomings, such as trapping at local optima. To solve this issue, hybrid SI methods can perform better than individual ones. In this study, we developed a boosted version of the reptile search algorithm (RSA) to be employed for different complex problems, such as intrusion detection systems (IDSs) in cloud–IoT environments, as well as different optimization and engineering problems. This modification was performed by employing the operators of the red fox algorithm (RFO) and triangular mutation operator (TMO). The aim of using the RFO was to boost the exploration of the RSA, whereas the TMO was used for enhancing the exploitation stage of the RSA. To assess the developed approach, called RSRFT, a set of six constrained engineering benchmarks was used. The experimental results illustrated the ability of RSRFT to find the solution to those tested engineering problems. In addition, it outperformed the other well-known optimization techniques that have been used to handle these problems.

Cited by 8Open access

Towards Visualization of Discrete Optimization Problems and Search Algorithms

Volke, Sebastian · Qucosa (Saxon State and University Library Dresden) · 2018

Diskrete Optimierung beschäftigt sich mit dem Identifizieren einer Kombination oder Permutation von Elementen, die im Hinblick auf ein gegebenes quantitatives Kriterium optimal ist. Anwendungen dafür entstehen aus Problemen in der Wirtschaft, der industriellen Fertigung, den Ingenieursdisziplinen, der Mathematik und Informatik. Dazu gehören unter anderem maschinelles Lernen, die Planung der Reihenfolge und Terminierung von Fertigungsprozessen oder das Layout von integrierten Schaltkreisen. Häufig sind diskrete Optimierungsprobleme NP-hart. Dadurch kommt der Erforschung effizienter, heuristischer Suchalgorithmen eine große Bedeutung zu, um für mittlere und große Probleminstanzen überhaupt gute Lösungen finden zu können. Dabei wird die Entwicklung von Algorithmen dadurch erschwert, dass Eigenschaften der Probleminstanzen aufgrund von deren Größe und Komplexität häufig schwer zu identifizieren sind. Ebenso herausfordernd ist die Analyse und Evaluierung von gegebenen Algorithmen, da das Suchverhalten häufig schwer zu charakterisieren ist. Das trifft besonders im Fall von emergentem Verhalten zu, wie es in der Forschung der Schwarmintelligenz vorkommt. Visualisierung zielt auf das Nutzen des menschlichen Sehens zur Datenverarbeitung ab. Das Gehirn hat enorme Fähigkeiten optische Reize von den Sehnerven zu analysieren, Formen und Muster darin zu erkennen, ihnen Bedeutung zu verleihen und dadurch ein intuitives Verstehen des Gesehenen zu ermöglichen. Diese Fähigkeit kann im Speziellen genutzt werden, um Hypothesen über komplexe Daten zu generieren, indem man sie in einem Bild repräsentiert und so dem visuellen System des Betrachters zugänglich macht. Bisher wurde Visualisierung kaum genutzt um speziell die Forschung in diskreter Optimierung zu unterstützen. Mit dieser Dissertation soll ein Ausgangspunkt geschaffen werden, um den vermehrten Einsatz von Visualisierung bei der Entwicklung von Suchheuristiken zu ermöglichen. Dazu werden zunächst die zentralen Fragen in der Algorithmenentwicklung diskutiert und daraus folgende Anforderungen an Visualisierungssysteme abgeleitet. Mögliche Forschungsrichtungen in der Visualisierung, die konkreten Nutzen für die Forschung in der Optimierung ergeben, werden vorgestellt. Darauf aufbauend werden drei Visualisierungssysteme und eine Analysemethode für die Erforschung diskreter Suche vorgestellt. Drei wichtige Aufgaben von Algorithmendesignern werden dabei adressiert. Zunächst wird ein System für den detaillierten Vergleich von Algorithmen vorgestellt. Auf der Basis von Zwischenergebnissen der Algorithmen auf einer Probleminstanz wird der Suchverlauf der Algorithmen dargestellt. Der Fokus liegt dabei dem Verlauf der Qualität der Lösungen über die Zeit, wobei die Darstellung durch den Experten mit zusätzlichem Wissen oder Klassifizierungen angereichert werden kann. Als zweites wird ein System für die Analyse von Suchlandschaften vorgestellt. Auf Basis von Pfaden und Abständen in der Landschaft wird eine Karte der Probleminstanz gezeichnet, die strukturelle Merkmale intuitiv erfassbar macht. Der zweite Teil der Dissertation beschäftigt sich mit der topologischen Analyse von Suchlandschaften, aufbauend auf einer Schwellwertanalyse. Ein Visualisierungssystem wird vorgestellt, dass ein topologisch equivalentes Höhenprofil der Suchlandschaft darstellt, um die topologische Struktur begreifbar zu machen. Dieses System ermöglicht zudem, den Suchverlauf eines Algorithmus direkt in der Suchlandschaft zu beobachten, was insbesondere bei der Untersuchung von Schwarmintelligenzalgorithmen interessant ist. Die Berechnung der topologischen Struktur setzt eine vollständige Aufzählung aller Lösungen voraus, was aufgrund der Größe der Suchlandschaften im allgemeinen nicht möglich ist. Um eine Anwendbarkeit der Analyse auf größere Probleminstanzen zu ermöglichen, wird eine Methode zur Abschätzung der Topologie vorgestellt. Die Methode erlaubt eine schrittweise Verfeinerung der topologischen Struktur und lässt sich heuristisch steuern. Dadurch können Wissen und Hypothesen des Experten einfließen um eine möglichst hohe Qualität der Annäherung zu erreichen bei gleichzeitig überschaubarem Berechnungsaufwand.

Cited by 0Open access

Chaotic hunger games search optimization algorithm for global optimization and engineering problems

Kutlu Onay, Funda, Aydemı̇r, Salih Berkan · RePEc: Research Papers in Economics

Chaotic maps have the characteristics of ergodicity and non-repeatability. Owing to these properties, they provide fast convergence by effectively scanning the search space in a metaheuristic optimization algorithm. The Hunger Games Search (HGS) is a metaheuristic algorithm modeled on the foraging and hunger instincts of animals. In this study, ten chaotic maps have been applied to the classical HGS method. The control of two random values in the HGS algorithm has been carried out with chaotic maps in three alternative scenarios. Accordingly, it has been observed that Scenario 2 exhibits a more stable and faster convergence than other scenarios. The performance of the proposed chaotic HGS has been evaluated on CEC2017 and 23 classical benchmark problems. The proposed algorithm has been applied to real engineering problems for cantilever beam design, tension/compression, and speed reducer, and the results have been compared with classical HGS and state-of-art algorithms in the literature. It can be seen that chaotic HGS yields promising results compared to other studies in the literature.

Cited by 0

Dynamic Grover Search: Applications in Recommendation systems and Optimization problems

Chakrabarty, Indranil, Khan, Shahzor, Singh, Vanshdeep · arXiv (Cornell University) · 2015

In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.

Cited by 0Open access

Atomic optimization. I. Search Space Transformation and One-dimensional Problems

Vladimir Pozdyayev · Automation and Remote Control · 2013

The paper studies optimization problems with polynomial objective functions and constraints in the form of inequalities. We propose a transformation of the solution method based on the theory of moments. This transformation allows to construct equivalent solution algorithms in the augmented original search space instead of the space of moments. Onedimensional optimization problems are explored in detail.

Cited by 7

Hybrid Optimization Coupling Electromagnetism and Descent Search for Engineering Problems

Ana Maria, A. C. Rocha, Edite M. G. P. Fern · 2015

In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mecha-nism with an approximate descent search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the constraints of the problem relies on a simple heuristic denoted by feasibility and dominance rules. The hybrid EM method is tested on four well-known engineering design problems and the numerical results demon-strate the effectiveness of the proposed approach. Key words: Hybrid methods, electromagnetism-like mechanism, descent search

Cited by 0

Student Attitude to Internet Search Engines: Navigation and Optimization Problems

Aida V. Kiryakova, Liudmila G. Shabalina, O. S. Manakova et al. · Propósitos y Representaciones · 2021

The article relevance. Currently, the world is rapidly undergoing the process of Informatization of all aspects of society, the development and introduction of new information technologies. This highlights the need for further reflection and research on the development of the Internet and its opening opportunities for people. The aim of the research is to study the peculiarities of the attitude of students to search and recommendation services on the Internet. Research methods: as a research method, we used a questionnaire survey as a method of collecting primary information, which allows us to identify various aspects related to the attitude of students to search and recommendation systems on the Internet. Research results: the article examines the specifics of Russian search and recommendation systems, students' attitude to these services, and their place in their lives. The novelty and originality of the research lies in the fact that for the first time the search and recommendation services of the Internet space were studied. It is shown that these services were initially developed in the sphere of culture and gradually began to spread to other spheres of people's life, which attracted e-Commerce figures. It is revealed that those students who discovered search and recommendation services a few years ago still use them to choose leisure activities. It is shown that students still identify some disadvantages of these systems: inaccurate recommendations, a large number of questions to determine preferences. There is some distrust to new Internet technologies among those who are used to relying on their intuition when choosing. It is determined that students often use search and recommendation services, since in most cases gadgets help them spend their free time, have fun: read a book, watch a movie, listen to music. It is revealed that the majority of students trust Internet services, although they are not always satisfied with the recommendations. It is shown that the level of student-user confidence in traditional advertising and marketing decreases simultaneously. It is determined that from the point of view of students, today not only printed versions of Newspapers, traditional radio, but even mass broadcast television are losing ground before the Internet as the most promising communication channel. Practical significance: the data Obtained in this work can be used in marketing research, economic Sciences, advertising psychology, as well as for further theoretical development of this issue.

Cited by 1Open access

A new gravitational search optimization algorithm to solve single and multiobjective optimization problems

Sajad Tabatabaei · Journal of Intelligent & Fuzzy Systems · 2014

This paper proposes a sufficient improved optimization algorithm based on gravitational search algorithm (GSA) to handle complex multi-objective (MO) optimization problems. The main idea behind the GSA is based on the Newton's law which has shown great success in recent years. Nevertheless, the existence of some deficiencies such as dependency of the algorithm on its adjusting parameters, stagnation and the probability of trapping in local optima has reduced the searching ability of the GSA. In order to overcome the above deficiencies, this paper suggests a novel mutation technique which is implemented based on four types of solutions. The first type belongs to the non-dominated solutions. The second type includes those solutions which are dominated by only one solution in the search space. Similarly, the third and the forth types belong to those solutions which are dominated by only three and four solutions respectively. After that, the initial GSA population is constructed by choosing from these four types of solutions. This procedure will let the next movement of the algorithm to reach more optimal solutions through the improvisation stage. The satisfying performance of the proposed algorithm is examined on several well-known benchmarks including both the single-objective and multi-objective optimization problems.

Cited by 4

Cuckoo search for inverse problems and simulated-driven shape optimization.

Xin‐She Yang · 2012

Most optimization problems rely heavily on simulations to evaluate design solutions according to a set of design criteria under stringent constraints. While an inverse problem may also rely on simulation-driven methods, it can sometimes be viewed more properly as a constrained optimization problem because the main aim of inversion is to find the best parameter estimates so as to minimize the differences between predicted results and observations. In this paper, we will take a unified approach to inversion and optimization. We use the latest cuckoo search to solve inverse problems and shape optimization in heat transfer applications. Simulation results show that cuckoo search is also very efficient for inversion.

Cited by 6

Local Search, data structures and Monte Carlo Search for Multi-Objective Combinatorial Optimization Problems

M. Max. Cornu · 2018

De nombreux problèmes d'optimisation combinatoire considèrent plusieurs objectifs, souvent conflictuels. Cette thèse s'intéresse à l'utilisation de méthodes de recherche locale, de structures de données et de recherche Monte-Carlo pour la recherche de l'ensemble des solutions efficaces de tels problèmes, représentant l'ensemble des meilleurs compromis pouvant être réalisés en considération de tous les objectifs.Nous proposons une nouvelle méthode d'approximation appelée 2-Phase Iterated Pareto Local Search based on Decomposition (2PIPLS/D) combinant les concepts de recherche locale Pareto (PLS) et de décomposition. La PLS est une descente de recherche locale adaptée au multi-objectif, et la décomposition consiste en la subdivision du problème multi-objectif en plusieurs problèmes mono-objectif. Deux méthodes d'optimisation mono-objectif sont considérées: la recherche locale itérée et la recherche Monte-Carlo imbriquée. Deux modules principaux sont intégrés à 2PIPLS/D. Le premier généralise et améliore une méthode existante et génère un ensemble initial de solutions. Le second réduit efficacement l'espace de recherche et permet d'accélérer la PLS sans négliger la qualité de l'approximation générée. Nous introduisons aussi deux nouvelles structures de données gérant dynamiquement un ensemble de solutions incomparables, la première est spécialisée pour le cas bi-objectif et la seconde pour le cas général.2PIPLS/D est appliquée au Problème du Voyageur de Commerce bi-objectif et tri-objectif et surpasse ses concurrents sur les instances testées. Ensuite, 2PIPLS/D est appliquée à un nouveau problème avec cinq objectifs en lien avec la récente réforme territoriale d'agrandissement des régions françaises.

Cited by 1Open access