ENHANCED META-HEURISTICS WITH VARIABLE NEIGHBORHOOD SEARCH STRATEGY FOR COMBINATORIAL OPTIMIZATION PROBLEMS

2016 ◽  
Vol 17 (2) ◽  
pp. 125-149
Author(s):  
Noureddine Bouhmala
Keyword(s):  
Combinatorial Optimization ◽  
Variable Neighborhood Search ◽  
Search Strategy ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Combinatorial Optimization Problems

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.

Theorems Supporting r-flip Search for Pseudo-Boolean Optimization

2012 ◽  
pp. 286-305
Author(s):  
Bahram Alidaee ◽  
Gary Kochenberger ◽  
Haibo Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Binary Variables ◽  
Preliminary Results ◽  
Combinatorial Optimization Problems ◽  
Neighborhood Structures ◽  
Heuristic Search Methods ◽  
Boolean Optimization ◽  
Simple Heuristics

Modern metaheuristic methodologies rely on well defined neighborhood structures and efficient means for evaluating potential moves within these structures. Move mechanisms range in complexity from simple 1-flip procedures where binary variables are “flipped” one at a time, to more expensive, but more powerful, r-flip approaches where “r” variables are simultaneously flipped. These multi-exchange neighborhood search strategies have proven to be effective approaches for solving a variety of combinatorial optimization problems. In this paper, we present a series of theorems based on partial derivatives that can be readily adopted to form the essential part of r-flip heuristic search methods for Pseudo-Boolean optimization. To illustrate the use of these results, we present preliminary results obtained from four simple heuristics designed to solve a set of Max 3-SAT problems.

scholarly journals A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Kai-Cheng Hu ◽  
Chun-Wei Tsai ◽  
Ming-Chao Chiang ◽  
Chu-Sing Yang
Keyword(s):  
Approximate Solution ◽  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
High Probability ◽  
Search Strategy ◽  
Optimization Problems ◽  
Clustering Algorithms ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Search Directions

Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

Theorems Supporting r-flip Search for Pseudo-Boolean Optimization

2010 ◽  
Vol 1 (1) ◽  
pp. 93-109 ◽  
Author(s):  
Bahram Alidaee ◽  
Gary Kochenberger ◽  
Haibo Wang
Keyword(s):  
Combinatorial Optimization ◽  
Heuristic Search ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Preliminary Results ◽  
Combinatorial Optimization Problems ◽  
Neighborhood Structures ◽  
Heuristic Search Methods ◽  
Boolean Optimization ◽  
Simple Heuristics

Modern metaheuristic methodologies rely on well defined neighborhood structures and efficient means for evaluating potential moves within these structures. Move mechanisms range in complexity from simple 1-flip procedures where binary variables are “flipped” one at a time, to more expensive, but more powerful, r-flip approaches where “r” variables are simultaneously flipped. These multi-exchange neighborhood search strategies have proven to be effective approaches for solving a variety of combinatorial optimization problems. In this paper, we present a series of theorems based on partial derivatives that can be readily adopted to form the essential part of r-flip heuristic search methods for Pseudo-Boolean optimization. To illustrate the use of these results, we present preliminary results obtained from four simple heuristics designed to solve a set of Max 3-SAT problems.

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.

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