scholarly journals Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

2017 ◽  
Author(s):  
Christos Papalitsas ◽  
Panayiotis Karakostas ◽  
Theodore Andronikos ◽  
Spyros Sioutas ◽  
Konstantinos Giannakis
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Real Life ◽  
Neighborhood Search ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Np Hard Problem ◽  
Gps System ◽  
Tools And Techniques

General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

scholarly journals Variable neighborhood search for minimum linear arrangement problem

2016 ◽  
Vol 26 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Nenad Mladenovic ◽  
Dragan Urosevic ◽  
Dionisio Pérez-Brito
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
State Of The Art ◽  
The State ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Np Hard ◽  
Linear Arrangement ◽  
Arrangement Problem ◽  
Linear Arrangement Problem

The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. It consists of finding an embedding of the nodes of a graph on the line such that the sum of the resulting edge lengths is minimized. This problem is one among the classical NP-hard optimization problems and therefore there has been extensive research on exact and approximative algorithms. In this paper we present an implementation of a variable neighborhood search (VNS) for solving minimum linear arrangement problem. We use Skewed general VNS scheme that appeared to be successful in solving some recent optimization problems on graphs. Based on computational experiments, we argue that our approach is comparable with the state-of-the-art heuristic.

Designing Parallel Meta-Heuristic Methods

2014 ◽  
pp. 260-280 ◽  
Author(s):  
Teodor Gabriel Crainic ◽  
Tatjana Davidović ◽  
Dušan Ramljak
Keyword(s):  
High Performance ◽  
Optimization Problems ◽  
Real Life ◽  
Neighborhood Search ◽  
Heuristic Methods ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
And Control ◽  
Reasonable Computing

Meta-heuristics represent powerful tools for addressing hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in “reasonable” computing times. Moreover, a major issue in meta-heuristic design and calibration is to provide high performance solutions for a variety of problems. Parallel meta-heuristics aim to address both issues. The objective of this chapter is to present a state-of-the-art survey of the main parallelization ideas and strategies, and to discuss general design principles applicable to all meta-heuristic classes. To achieve this goal, the authors explain various paradigms related to parallel meta-heuristic development, where communications, synchronization, and control aspects are the most relevant. They also discuss implementation issues pointing out the characteristics of shared and distributed memory multiprocessors as target architectures. All these topics are illustrated by the examples from recent literature related to the parallelization of various meta-heuristic methods. Here, the authors focus on Variable Neighborhood Search and Bee Colony Optimization.

scholarly journals A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

2021 ◽  
Author(s):  
omar cheikhrouhou
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Real Life ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Comprehensive Survey ◽  
Multiple Travelling Salesman Problem

The Multiple Travelling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, transportation, networking, etc.Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.

scholarly journals Multivalued decision diagrams for prize-collecting job sequencing with one common and multiple secondary resources

2019 ◽  
Author(s):  
Johannes Maschler ◽  
Günther R. Raidl
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Decision Diagrams ◽  
Top Down ◽  
Combinatorial Optimization Problems ◽  
Job Sequencing ◽  
Refinement Method ◽  
Prize Collecting ◽  
Dual Bounds

AbstractMultivalued decision diagrams (MDD) are a powerful tool for approaching combinatorial optimization problems. Relatively compact relaxed and restricted MDDs are applied to obtain dual bounds and heuristic solutions and provide opportunities for new branching schemes. We consider a prize-collecting sequencing problem in which a subset of given jobs has to be found that is schedulable and yields maximum total prize. The primary aim of this work is to study different methods for creating relaxed MDDs for this problem. To this end, we adopt and extend the two main MDD compilation approaches found in the literature: top down construction and incremental refinement. In a series of computational experiments these methods are compared. The results indicate that for our problem the incremental refinement method produces MDDs with stronger bounds. Moreover, heuristic solutions are derived by compiling restricted MDDs and by applying a general variable neighborhood search (GVNS). Here we observe that the top down construction of restricted MDDs is able to yield better solutions as the GVNS on small to medium-sized instances.

scholarly journals A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

2021 ◽  
Author(s):  
omar cheikhrouhou
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Real Life ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Comprehensive Survey ◽  
Multiple Travelling Salesman Problem

The Multiple Travelling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, transportation, networking, etc.Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.

scholarly journals A Novel Metaheuristic for Travelling Salesman Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Vahid Zharfi ◽  
Abolfazl Mirzazadeh
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Network Routing ◽  
Np Hard ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Hard Problems ◽  
Insight Into ◽  
Np Hard Problems

One of the well-known combinatorial optimization problems is travelling salesman problem (TSP). This problem is in the fields of logistics, transportation, and distribution. TSP is among the NP-hard problems, and many different metaheuristics are used to solve this problem in an acceptable time especially when the number of cities is high. In this paper, a new meta-heuristic is proposed to solve TSP which is based on new insight into network routing problems.

scholarly journals ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1456
Author(s):  
Stefka Fidanova ◽  
Krassimir Todorov Atanassov
Keyword(s):  
Decision Making ◽  
Knapsack Problem ◽  
Propositional Logic ◽  
Optimization Problems ◽  
Real Life ◽  
Combinatorial Optimization Problems ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Multiple Constraint

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

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