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9th World Conference on Information Systems and Technologies (WorldCIST)

作者： Dahmani, Nadia Krichen, Saoussen Talbi, El-Ghazali Kaddoura, Sanaa Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Univ Tunis Inst Super Gest Tunis LARODEC Tunis Tunisia Univ Lille 1 LIFL CNRS UMR 8022 INRIA Lille Nord Europe Villeneuve Dascq France

ISBN: (纸本)9783030726539;9783030726546

In this paper, an exact method is designed to solve the multi-objective 2-dimensional vector packing problem. The algorithm is an adapted version of an efficient epsilon-constraint method which proves its efficiency in solving a large variety of multi-objective optimization problems. This method is based on a clever decomposition of the initial problem into sub-problems which are iteratively solved through mathematical programming. To accelerate the search process, we propose a new integer programming model for solving the multi-objective 2-dimensional vector packing problem based on the compact model for the bin packing problem with fragile objects. Instead of scanning all possible solutions, we consider the solutions while solving a Subset-Sum Problem. Hence, nonuseful subproblems are avoided and thus the search space is reduced. An experimental study is performed based instances from the literature. A comparison between the exact method and a grounded metaheuristic which provides good results in solving the multi-objective 2-dimensional vector packing problem.

关键词： 2-dimensional vector packing problem multi-objective combinatorial optimization epsilon-constraint method Metaheuristics

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Geometric Particle Swarm optimization for multi-objective optimization Using Decomposition 16

Geometric Particle Swarm Optimization for Multi-objective Op...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Zapotecas-Martinez, Saul Moraglio, Alberto Aguirre, Hernan E. Tanaka, Kiyoshi Shinshu Univ Fac Engn 4-17-1 Wakasato Nagano 3808553 Japan Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

ISBN: (纸本)9781450342063

multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm optimization (PSO) has become one of the most popular single-objective optimizers for continuous problems, and recently it has been successfully extended to the multi-objective domain. However, no investigation on the application of PSO within a multi-objective decomposition framework exists in the context of combinatorial optimization. This is precisely the focus of the paper. More specifically, we study the incorporation of Geometric Particle Swarm optimization (GPSO), a discrete generalization of PSO that has proven successful on a number of single-objective combinatorial problems, into a decomposition approach. We conduct experiments on many objective 1/0 knapsack problems i.e. problems with more than three objectives functions, substantially harder than multi-objective problems with fewer objectives. The results indicate that the proposed multi-objective GPSO based on decomposition is able to outperform two version of the well-know MOEA based on decomposition (MOEA/D) and the most recent version of the non-dominated sorting genetic algorithm (NSGA-III), which are state-of-the-art multi-objective evolutionary approaches based on decomposition.

关键词： multi-objective combinatorial optimization Decomposition-based MOEAs Particle Swarm optimization

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multi-objective Optimisation of Wireless Sensor Networks Deployment: Application to fire surveillance in smart car parks 17

Multi-objective Optimisation of Wireless Sensor Networks Dep...

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17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Benghelima, Slimane Charafeddine Ould-Khaoua, Mohamed Benzerbadj, Ali Baala, Oumaya Univ Blida 1 Dept Comp Sci LRDSI Blida Algeria Ain Temouchent Univ Belhadj Bouchaib Dept Math & Comp Sci Ain Temouchent Algeria Res Lab Ind Comp & Networks RIIR Oran Algeria Univ Bourgogne Franche Comte FEMTO ST Inst CNRS UTBM Belfort France

ISBN: (纸本)9781728186160

The exponential growth of the Internet-of-Things (IoT) technology paradigm has resulted in new applications and on-line services. Smart car park is one interesting example among others that can take advantage of applications based on wireless sensor networks (WSNs) Which constitute the core of IoT. This paper focuses on the deployment optimization problem of WSNs dedicated to the fire detection in a smart car park. In such networks, the nodes are classified into two categories: Sensor Nodes (SNs) deployed within the smart car park for targets coverage and Relay Nodes (RNs) whose task is to relay alert messages generated by the sensor nodes up to the sink node. In this study, we propose a multi-objective Binary Integer Linear Programming (MOBILP) which minimizes simultaneously the number of sensor nodes, relay nodes and the maximum distance from sensor nodes to the sink node, while ensuring coverage and connectivity. We have conducted extensive tests in order to evaluate the performance of our proposal. The results demonstrate that the MOBILP outperforms the existing approaches in terms of quality of solutions compared to a sequential deployment method, which consists to deploy SNs then RNs, and in terms of the ability to find other efficient solutions compared to a simultaneous deployment method using a mono-objective function, which consists to deploy SNs and RNs simultaneously.

关键词： Internet of Things Wireless Sensor Networks Deterministic Deployment multi-objective combinatorial optimization Smart Car Parks Fire Surveillance

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Dominance, Epsilon, and Hypervolume Local Optimal Sets in multi-objective optimization, and How to Tell the Difference 18

Dominance, Epsilon, and Hypervolume Local Optimal Sets in Mu...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Liefooghe, Arnaud Lopez-Ibanez, Manuel Paquete, Luis Verel, Sebastien Univ Lille Inria Lille Nord Europe CNRS UMR 9189 CRIStAL F-59000 Lille France Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Coimbra DEI CISUC Coimbra Portugal Univ Littoral Cote dOpale LISIC F-62100 Calais France

ISBN: (纸本)9781450356183

Local search algorithms have shown good performance for several multi-objective combinatorial optimization problems. These approaches naturally stop at a local optimal set (LO-set) under given definitions of neighborhood and preference relation among subsets of solutions, such as set-based dominance relation, hypervolume or epsilon indicator. It is an open question how LO-sets under different set preference relations relate to each other. This paper reports an in-depth experimental analysis on multi-objective nk-landscapes. Our results reveal that, whatever the preference relation, the number of LO-sets typically increases with the problem non-linearity, and decreases with the number of objectives. We observe that strict LO-sets of bounded cardinality under set-dominance are LO-sets under both epsilon and hypervolume, and that LO-sets under hypervolume are LO-sets under set-dominance, whereas LO-sets under epsilon are not. Nonetheless, LO-sets under set-dominance are more similar to LO-sets under epsilon than under hypervolume. These findings have important implications for multi-objective local search. For instance, a dominance-based approach with bounded archive gets more easily trapped and might experience difficulty to identify an LO-set under epsilon or hypervolume. On the contrary, a hypervolume-based approach is expected to perform more steps before converging to better approximations.

关键词： multi-objective combinatorial optimization Local search Local optima Set-based multi-objective optimization Quality Indicators

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multi-objective WSN Deployment: Quality of Monitoring, Connectivity and Lifetime

Multi-Objective WSN Deployment: Quality of Monitoring, Conne...

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2010 IEEE International Conference on Communications

作者： Aitsaadi, Nadjib Achir, Nadjib Boussetta, Khaled Pujolle, Guy Univ Paris 06 LIP6 104 Ave President Kennedy F-75016 Paris France Univ Paris 13 L2TI Inst Galilee F-93430 Villetaneuse France

ISBN: (纸本)9781424464043

In this paper, we will address a WSN deployment problem. The main objectives are i) reduce the cost of deployment, ii) ensure the requested event detection probabilities, iii) guarantee the network connectivity, and iv) maximize the lifetime of the network. We will formalize the problem as multi-objective combinatorial optimization problem. To resolve the problem, we will propose a new deployment algorithm named MODA. It will be based on evolutionary and neighborhood search algorithms. The obtained results are better than the deployment strategies found in the literature.

关键词： WSN deployment QoM Connectivity Lifetime multi-objective combinatorial optimization

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Martins' algorithm revisited for multi-objective shortest path problems with a MaxMin cost function

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4OR 2006年第1期4卷 47-59页

作者： Gandibleux, Xavier Beugnies, Frédéric Randriamasy, Sabine LINA - FRE CNRS 2729 Université de Nantes 44322 Nantes Cedex 03 2 rue de la Houssiniere France LAMIH-ROI - UMR CNRS 8530 Université de Valenciennes Campus le Mont Houy 59313 Valenciennes Cedex 9 France ALCATEL RandI - ISR Project 91460 Marcoussis Route de Nozay France

This paper presents a direct extension of the label setting algorithm proposed by Martins in 1984 for the shortest path problem with multiple objectives. This extended version computes all the efficient paths from a given source vertex, to all the other vertices of the network. The algorithm copes with problems in which the "cost values" associated with the network arcs are positive. The proposed extension can handle objective functions that are either of the "sum" type or of the "bottleneck" type. The main modifications to Martins' algorithm for multi-objective shortest path problems are linked to the dominance test and the procedure for identifying efficient paths. The algorithmic features are described and a didactic example is provided to illustrate the working principle. The results of numerical experiments concerning the number of efficient solutions produced and the CPU time consumed for several configurations of objectives, on a set of randomly generated networks, are also provided. © Springer-Verlag 2006.

关键词： Labelling algorithm multi-objective combinatorial optimization Shortest path problem

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multi-objective Large Neighborhood Search

Multi-Objective Large Neighborhood Search

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19th Annual International Conference on Principles and Practice of Constraint Programming (CP)

作者： Schaus, Pierre Hartert, Renaud UCLouvain ICTEAM B-1348 Louvain Belgium

ISBN: (纸本)9783642406263;9783642406270

Large neighborhood search (LNS) [25] is a framework that combines the expressiveness of constraint programming with the efficiency of local search to solve combinatorial optimization problems. This paper introduces an extension of LNS, called multi-objective LNS (MO-LNS), to solve multi-objective combinatorial optimization problems ubiquitous in practice. The idea of MO-LNS is to maintain a set of nondominated solutions rather than just one best-so-far solution. At each iteration, one of these solutions is selected, relaxed and optimized in order to strictly improve the hypervolume of the maintained set of nondominated solutions. We introduce modeling abstractions into the OscaR solver for MO-LNS and show experimentally the efficiency of this approach on various multi-objective combinatorial optimization problems.

关键词： Constraint Programming multi-objective combinatorial optimization Large Neighborhood Search

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multi-objective Factored Evolutionary optimization and the multi-objective Knapsack Problem

Multi-Objective Factored Evolutionary Optimization and the M...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Peerlinck, Amy Sheppard, John Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

We propose a factored evolutionary framework for multi-objective optimization that can incorporate any multi-objective population based algorithm. Our framework, which is based on Factored Evolutionary Algorithms, uses overlapping subpopulations to increase exploration of the objective space;however, it also allows for the creation of distinct subpopulations as in co-operative co-evolutionary algorithms (CCEA). We apply the framework with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), resulting in Factored NSGA-II. We compare NSGA-II, CC-NSGA-II, and F-NSGA-II on two different versions of the multi-objective knapsack problem. The first is the classic binary multi-knapsack implementation introduced by Zitzler and Thiele, where the number of objectives equals the number of knapsacks. The second uses a single knapsack where, aside from maximizing profit and minimizing weight, an additional objective tries to minimize the difference in weight of the items in the knapsack, creating a balanced knapsack. We further extend this version to minimize volume and balance the volume. The proposed 3-to-5 objective balanced single knapsack problem poses a difficult problem for multi-objective algorithms. Our results indicate that the non-dominated solutions found by F-NSGA-II tend to cover more of the Pareto front and have a larger hypervolume.

关键词： multi-objective combinatorial optimization co-operative coevolution non-dominated sorting genetic algorithm multi-objective knapsack

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A Bi-objective Iterated Local Search Heuristic with Path-Relinking for the p-Median Problem

A Bi-objective Iterated Local Search Heuristic with Path-Rel...

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6th International Conference on Evolutionary multi-Criterion optimization

作者： Arroyo, Jose E. C. Santos, Andre G. dos Santos, Paula M. Ribeiro, Wellington G. Univ Fed Vicosa Dept Informat BR-3657000 Ctr Vicosa MG Brazil

ISBN: (纸本)9783642198922

In this paper, we examine the p-median problem from a bi-objective point of view. Since this is a NP-Hard problem, an efficient algorithm based on the Iterated Local Search heuristic (ILS) is proposed to determine non-dominated solutions (an approximation of the Pareto-optimal solutions). ILS is a simple and powerful stochastic method that has shown very good results for a variety of single-objective combinatorial problems. In each component of the ILS, we use the concept of Pareto dominance. An intensification component based on the Path-Relinking is used to improve the quality of the found non-dominated solutions. To test the performance of the proposed algorithm, we develop a Mathematical Programming Algorithm, called c-Constraint, that finds a subset of Pareto-optimal solutions by solving iteratively the mathematical model of the problem with additional constraints. The results show that the proposed approach is able to generate good approximations to the non-dominated frontier of the bi-objective problem efficiently.

关键词： Heuristics Local search Path Relinking multi-objective combinatorial optimization p-Medians

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Constraint-based electoral districting using a new compactness measure: Anapplication to Portugal

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COMPUTERS & OPERATIONS RESEARCH 2022年 146卷

作者： Almeida, Tiago Manquinho, Vasco Univ Lisbon INESC ID Inst Super Tecn Lisbon Portugal INESC ID Rua Alves Redol 9 P-1000029 Lisbon Portugal

Since the 1960s, several approaches have been proposed to prevent the redrawing of electoral districts so thatthey are beneficial to a particular party or political faction (gerrymandering). In order to avoid gerrymandering,most approaches focus solely on redrawing electoral maps that try to maximize the compactness of the electoraldistricts. However, this problem is computationally hard, and several criteria must be satisfied when drawingelectoral *** this work we focus on drawing electoral districts for Portugal. For that, a new compact Booleanformulation is proposed for solving the electoral districting problem. This formulation satisfies all standardcriteria for building electoral maps, particularly the contiguity and representation criteria. Moreover, a newcompactness measure is also proposed that does not depend on geographic centers. Additionally, a heuristicformulation is also devised for problem instances where the optimum values are hard to *** new compactness measure and formulations are applied in the context of Portuguese parliamentelections, assuming a change in the Portuguese electoral system. Experimental results on drawing electoralmaps show that the proposed formulations are more effective than previous ones in drawing the electoraldistricts in the Portuguese context. Moreover, based on results from the last elections, gerrymandering scenariosare devised, showing that electoral outcomes can change depending on the drawing of the electoral maps.

关键词： Electoral districting Gerrymandering multi-objective combinatorial optimization Compactness

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