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Solving Assignment Problems via Quantum Computing: a Case-Study in Train Seating Arrangement

Gioda Ilaria, Caputo Davide, Fadda Edoardo, Manerba Daniele, Silva Fernández Blanca, Tadei Roberto

Annals of Computer Science and Information Systems

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2021

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Vol. 25

217--220

EN

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should resolve complex problems currently considered to be intractable. This new research area is called Quantum Computing. What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems. This work focuses on solving assignment-like combinatorial optimization problems by exploiting this novel computational approach. A case-study, denoted as the Seating Arrangement Optimization problem, is considered. It is modeled through the Quadratic Unconstrained Binary Optimization paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv, and a quantum-classical hybrid system. The obtained experimental results are compared in terms of solution quality and computational efficiency

2

Dynamic Communication Topologies for Distributed Heuristics in Energy System Optimization Algorithms

Holly Stefanie, Nieße Astrid

Annals of Computer Science and Information Systems

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2021

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Vol. 25

191--200

EN

The communication topology is an essential aspect in designing distributed optimization heuristics. It can influence the exploration and exploitation of the search space and thus the optimization performance in terms of solution quality, convergence speed and collaboration costs - relevant aspects for applications operating critical infrastructure in energy systems. In this work, we present an approach for adapting the communication topology during runtime, based on the principles of simulated annealing. We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization heuristic. Finally, we investigate the correlations between fitness landscape properties and defined performance metrics.

3

Stochastic multi-depot vehicle routing problem with pickup and delivery: an ILS approach

Rios Brenner H. O., Xavier Eduardo C., Miyazawa Flávio K., Amorim Pedro

Annals of Computer Science and Information Systems

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2020

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Vol. 21

307--315

EN

We present a natural probabilistic variation of the multi-depot vehicle routing problem with pickup and delivery (MDVRPPD). In this paper, we present a variation of this deterministic problem, where each pair of pickup and delivery points are present with some probability, and their realization are only known after the routes are computed. We denote this stochastic version by S-MDVRPPD. One route for each depot must be computed satisfying precedence constraints, where each pickup point must appear before its delivery pair in the route. The objective is to find a solution with minimum expected traveling distance. We present a closed-form expression to compute the expected length of an a priori route under general probabilistic assumptions. To solve the S-MDVRPPD we propose an Iterated Local Search (ILS) that uses the Variable Neighborhood Descent (VND) as local search procedure. The proposed heuristic was compared with a Tabu Search (TS) algorithm based on a previous work. We evaluate the performance of these heuristics on a data set adapted from TSPLIB instances. The results show that the ILS proposed is efficient and effective to solve S-MDVRPPD.

4

On Finding the Optimal Tree of a Complete Weighted Graph

Hosseini Seyed Soheil, Wormald Nick, Tian Tianhai

Annals of Computer Science and Information Systems

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2020

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Vol. 21

271--275

EN

We want to find a tree where the path length between any two vertices on this tree is as close as possible to their corresponding distance in the complete weighted graph of vertices upon which the tree is built. We use the residual sum of squares as the optimality criterion to formulate this problem, and use the Cholesky decomposition to solve the system of linear equations to optimise weights of a given tree. We also use two metaheuristics, namely Simulated Annealing (SA) and Iterated Local Search (ILS) to optimise the tree structure. Our results suggest that SA and ILS both perform well at finding the optimal tree structure when the dispersion of distances in the complete graph is large. However, when the dispersion of distances is small, only ILS has a solid performance.

5

An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

Alihodzic Adis, Delalic Sead, Gusic Dzenan

Annals of Computer Science and Information Systems

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2020

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Vol. 21

207--214

EN

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

6

A Reactive Search-Based Algorithm for Scheduling Multiprocessor Tasks on Two Dedicated Processors

Aïder Méziane, Baatout Fatma Zohra, Hifi Mhand

Annals of Computer Science and Information Systems

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2020

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Vol. 21

257--261

EN

In this paper, we propose a reactive search-based algorithm for solving the problem of scheduling multiprocessor tasks on two dedicated processors. An instance of the problem is characterized by a set of tasks divided into three subsets and two processors, where some tasks can be executed either on one processor or two processors. The goal of the problem is to determine the scheduling of all tasks minimizing the execution of the last assigned task. The proposed reactive search starts with a starting greedy solution. Next, a series of local operators combined with a tabu list are introduced in order to intensify the search process. The method is also reinforced with a drop and rebuild operator that is applied for diversifying the search process. Finally, the performance of the proposed method is evaluated on a set of benchmark instances, where its provided results are compared to those achieved by a recent method available in the literature. Encouraging results have been reached.

7

Best response dynamics for VLSI physical design placement

Rapoport Michael, Tamir Tami

Annals of Computer Science and Information Systems

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2019

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Vol. 18

147--156

EN

The physical design placement problem is one of the hardest and most important problems in micro chips production. The placement defines how to place the electrical components on the chip. We consider the problem as a combinatorial optimization problem, whose instance is defined by a set of $2$-dimensional rectangles, with various sizes and wire connectivity requirements. We focus on minimizing the placement area and the total wire length.

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An efficient exhaustive search for the discretizable Distance Geometry Problem with interval data

Mucherino A., Lin J.-H.

Annals of Computer Science and Information Systems

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2019

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Vol. 18

135--141

EN

The Distance Geometry Problem (DGP) asks whether a simple weighted undirected graph can be realized in a given space (generally Euclidean) so that a given set of distance constraints (associated to the edges of the graph) is satisfied. The Discretizable DGP (DDGP) represents a subclass of instances where the search space can be reduced to a discrete domain having the structure of a tree. In the ideal case where all distances are precise, the tree is binary and one singleton, representing one possible position for a vertex of the graph, is associated to every tree node. When the distance information is however not precise, the uncertainty on the distance values implies that a three-dimensional region of the search space needs to be assigned to some nodes of the tree. By using a recently proposed coarse-grained representation for DDGP solutions, we extend in this work the branch-and-prune (BP) algorithm so that it can efficiently perform an exhaustive search of the search domain, even when the uncertainty on the distances is important. Instead of associating singletons to nodes, we consider a pair consisting of a box and of a most-likely position for the vertex in this box. Initial estimations of the vertex positions in every box can be subsequently refined by using local optimization. The aim of this paper is two-fold: (i) we propose a new simple method for the computation of the three-dimensional boxes to be associated to the nodes of the search tree; (ii) we introduce the resolution parameter ρ, with the aim of controling the similarity between pairs of solutions in the solution set. Some initial computational experiments show that our algorithm extension, differently from previously proposed variants of the BP algorithm, is actually able to terminate the enumeration of the solution set by providing solutions that differ from one another accordingly to the given resolution parameter.