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An adaptive heuristic for multi-objective controller placement in software-defined networks

COMPUTERS & ELECTRICAL ENGINEERING 2018年 66卷 204-228页

作者： Ahmadi, Vahid Khorramizadeh, Mostafa Shiraz Univ Technol Dept Math Shiraz Iran

Software-defined networking paradigm faces many challenges, including reliability, resiliency, scalability, and availability. These challenges can be tackled by carefully selecting placements within the network. However, the evaluation of all placements is only practical for small networks. In this paper, a fast and efficient adaptation of evolutionary algorithms is presented to solve large-scale multi-objective controller placement problems. The presented algorithm requires reasonable memory resource and enjoys a greedy heuristic to generate a high-quality initial population, smart mechanisms to encourage the diversification and intensification, and a new fast Pareto finder. Moreover, a new variant of the problem is developed in which the capacities of controllers and loads of switches are added as constraints. A new constraint handling technique is applied to adapt our algorithm to solve the new problem. Finally, the results on several topologies from Internet Topology Zoo revealed that our presented algorithms outperformed some other efficient algorithms from the literature. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Software-defined network Controller placement multi-objective combinatorial optimization Heuristic algorithms Pareto front multi-start hybrid non-dominated sorting genetic algorithm

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multi-objective process design in multi-purpose batch plants using a Tabu Search optimization algorithm

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COMPUTERS & CHEMICAL ENGINEERING 2004年第4期28卷 459-478页

作者： Cavin, L Fischer, U Glover, F Hungerbühler, K ETHZ Dept Chem Engn CH-8093 Zurich Switzerland Univ Colorado Leeds Sch Business Boulder CO 80309 USA

Chemical batch processes are typically used for the production of speciality chemicals and pharmaceuticals. Due to the still growing importance of this type of processing, design methods are required that take into account the special requirements and constraints in the corresponding production facilities. We developed a method that optimizes the design of a single chemical process to be implemented in an existing multi-purpose batch plant, in which a well-defined set of equipment units is available for realizing this process. In the optimization, three objectives with different priorities are considered. A flexible metaheuristic algorithm, Tabu Search (TS), has been implemented to solve this multi-objective combinatorial non-linear problem. We started from a basic form of TS to determine the effectiveness of this version as well as establish the relative strengths and weaknesses of first level TS strategies. Our investigation includes a thorough examination of algorithm parameters and of implementation issues to identify algorithm settings that can handle the whole class of problems considered. Overall, we concluded that the basic form of TS-using fixed default settings-exhibits highly attractive performance features for solving the problems at hand. Moreover, comparison with a multi-start steepest descent algorithm shows that a basic TS approach conducts a global search more effectively. As illustrated by three case studies, the new method is well suited for identifying optimal designs of a chemical process to be implemented in an existing multi-purpose batch plant. The approach is particularly suited for considering multiple prioritized objectives and for enabling the use of external (e.g. commercial) batch process simulation software as a black-box model for the process evaluations. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： batch process design multi-objective combinatorial optimization Tabu Search multi-purpose plant

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Self-adaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem

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APPLIED SOFT COMPUTING 2014年第0期16卷 124-136页

作者： Dahmani, Nadia Clautiaux, Francois Krichen, Saoussen Talbi, El-Ghazali LARODEC Inst Super Gest Tunis Cite Bouchoucha 2000 Le Bardo Tunisia Univ Sci & Technol Lille LIFL CNRS UMR 8022 INRIA Lille Nord Europe F-59655 Villeneuve Dascq France

In this paper, a multi-objective 2-dimensional vector packing problem is presented. It consists in packing a set of items, each having two sizes in two independent dimensions, say, a weight and a length into a finite number of bins, while concurrently optimizing three cost functions. The first objective is the minimization of the number of used bins. The second one is the minimization of the maximum length of a bin. The third objective consists in balancing the load overall the bins by minimizing the difference between the maximum length and the minimum length of a bin. Two population-based metaheuristics are performed to tackle this problem. These metaheuristics use different indirect encoding approaches in order to find good permutations of items which are then packed by a separate decoder routine whose parameters are embedded in the solution encoding. It leads to a self-adaptive metaheuristic where the parameters are adjusted during the search process. The performance of these strategies is assessed and compared against benchmarks inspired from the literature. (C) 2013 Elsevier B. V. All rights reserved.

关键词： 2-Dimensional vector packing problem multi-objective combinatorial optimization Metaheuristics

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Genetic local search for multi-objective flowshop scheduling problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2005年第3期167卷 717-738页

作者： Arroyo, JEC Armentano, VA Univ Estadual Campinas Fac Elect & Comp Engn Dept Engn Sistemas BR-13083970 Campinas SP Brazil Univ Colorado Coll Business & Adm Boulder CO 80309 USA

This paper addresses flowshop scheduling problems with multiple performance criteria in such a way as to provide the decision maker with approximate Pareto optimal solutions. Genetic algorithms have attracted the attention of researchers in the nineties as a promising technique for solving multi-objective combinatorial optimization problems. We propose a genetic local search algorithm with features such as preservation of dispersion in the population, elitism, and use of a parallel multi-objective local search so as intensify the search in distinct regions. The concept of Pareto dominance is used to assign fitness to the solutions and in the local search procedure. The algorithm is applied to the flowshop scheduling problem for the following two pairs of objectives: (i) makespan and maximum tardiness;(ii) makespan and total tardiness. For instances involving two machines, the algorithm is compared with Branchand-Bound algorithms proposed in the literature. For such instances and larger ones, involving up to 80 jobs and 20 machines, the performance of the algorithm is compared with two multi-objective genetic local search algorithms proposed in the literature. Computational results show that the proposed algorithm yields a reasonable approximation of the Pareto optimal set. (C) 2004 Elsevier B.V. All rights reserved.

关键词： multi-objective combinatorial optimization metaheuristics genetic local search flowshop scheduling

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multi-objective tabu search using a multinomial probability mass function

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第3期169卷 918-931页

作者： Kulturel-Konak, S Smith, AE Norman, BA Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Penn State Berks Lehigh Valley Coll Reading PA 19610 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA

A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible. (c) 2004 Elsevier B.V. All rights reserved.

关键词： multi-objective combinatorial optimization Tabu search

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NSGAII With Local Search Based Heavy Perturbation for Bi-objective Weighted Clique Problem

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IEEE ACCESS 2018年 6卷 51253-51261页

作者： Cai, Dunbo Gao, Yuhui Yin, Minghao Wuhan Inst Technol Hubei Prov Key Lab Intelligent Robot Wuhan 430205 Hubei Peoples R China Natl Key Lab Sci & Technol Aerosp Flight Dynam Beijing 100094 Peoples R China Beijing Aerosp Control Ctr Beijing 100094 Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China

This paper focuses on the clique problem of a weighted graph where weights of vertices can be either positive or negative (WG-PN). Two objectives, the size and the total weight of a clique, are considered. In particular, the larger size in terms of set inclusion and the higher total weight a clique is, the better it is. This problem is termed BiOWCP which is a multi-objective combinatorial optimization problem that contains confiict objectives. We show that BiOWCP cannot be translated into the clique problem of a weighted graph with positive vertex weights (WG-P) via a straight forward transformation, which highlights the difficulty of BiOWCP. We propose a heavy perturbation (HP)-based NSGAII (nondominated sorting genetic algorithm II) algorithm HP-NSGAII for the problem, where the perturbation is done by either improving a selected elitist with a local search procedure or swapping its left and right parts. Benchmarks for BiOWCP were adapted from the DIMACS set. Extensive experiments were carried out for different ratios of the heavy perturbation effort to the total search effort. Computational results show that HP-NSGAII is superior to NSGAII on many problems with respect to the hyper volume indicator and the set coverage indicator.

关键词： Clique problem evolutionary algorithm multi-objective combinatorial optimization negative vertex weight weighted graph

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optimization of workcell layout for hybrid medical device fabrication

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JOURNAL OF MANUFACTURING SYSTEMS 2019年 50卷 163-179页

作者： Ho, Nicholas Ngooi, Syn-Dee Chui, Chee-Kong Natl Univ Singapore Dept Mech Engn Singapore Singapore

Manufacturers often overlook the importance of optimizing workcell layout. Optimizing manufacturing workcell layouts can help increase production efficiency by reducing unnecessary actions and movements. However, the application of conventional optimization methods to modern production lines is not only becoming too complex, but also becoming inaccurate and unreliable. In this paper, we proposed an accurate and practical methodology that is readily implementable. The methodology uses multi-objective combinatorial optimization (MOCO) concepts to optimize manufacturing workcell layouts, specifically for the production of hybrid medical devices. The proposed model, also known as the Two-Step MOCO model, considers objectives and constraints that are selected by the manufacturer. The first step determines the optimal number of equipment and operators required given a fixed budget, while the second step determines the optimal positions for every equipment, given a fixed space and the required safety distance among each equipment, after considering the results of the first step. We illustrated the implementation of the proposed model on the fabrication of a hybrid medical device. Virtual manufacturing workcells were developed and utilized to evaluate the proposed methodology. The evaluation results proved the feasibility and effectiveness of the proposed model, and its ability to provide flexibility to manufacturers. This paper highlights the potential of our proposed optimization model in improving production efficiency in medical device and other manufacturing industries.

关键词： multi-objective combinatorial optimization Workcell layout Virtual manufacturing technology Medical devices

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Perturbed Decomposition Algorithm applied to the multi-objective Traveling Salesman Problem

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COMPUTERS & OPERATIONS RESEARCH 2017年 79卷 314-330页

作者： Cornu, Marek Cazenave, Tristan Vanderpooten, Daniel PSL Res Univ Univ Paris Dauphine CNRS UMR 7243LAMSADE F-75016 Paris France

Dealing with multi-objective combinatorial optimization, this article proposes a new multi-objective set based meta-heuristic named Perturbed Decomposition Algorithm (PDA). Combining ideas from decomposition methods, local search and data perturbation, PDA provides a 2-phase modular framework for finding an approximation of the Pareto front. The first phase decomposes the search into a number of linearly aggregated problems of the original multi-objective problem. The second phase conducts an iterative process: aggregated problems are first perturbed then selected and optimized by an efficient single-objective local search solver. Resulting solutions will serve as a starting point of a multi-objective local search procedure, called Pareto Local Search. After presenting a literature review of meta-heuristics on the multi-objective symmetric Traveling Salesman Problem (TSP), we conduct experiments on several instances of the bi-objective and tri-objective TSP. The experiments show that our proposed algorithm outperforms the best current methods on this problem. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-objective combinatorial optimization multi-objective Traveling Salesman Problem Meta-heuristics Pareto Local search Decomposition algorithm Data perturbation

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multi-objective matroid optimization with ordinal weights

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DISCRETE APPLIED MATHEMATICS 2023年 335卷 104-119页

作者： Klamroth, Kathrin Stiglmayr, Michael Sudhoff, Julia Univ Wuppertal Gaussstr 20 D-42119 Wuppertal Germany

Bi-objective optimization problems on matroids are in general intractable and their corresponding decision problems are in general NP-hard. However, if one of the ob-jective functions is restricted to binary cost coefficients the problem becomes efficiently solvable by an efficient swap algorithm. Binary cost coefficients often represent two categories and are thus a special case of ordinal coefficients that are in general *** this paper we consider ordinal objective functions with more than two categories in the context of matroid optimization. We introduce several problem variants that can be distinguished w.r.t. their respective optimization goals, analyze their interrelations, and derive a polynomial time solution method that is based on the repeated solution of matroid intersection problems. Numerical tests on minimum spanning tree problems and on partition matroids confirm the efficiency of the approach.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Matroid intersection multi-objective combinatorial optimization Ordinal weights multi-objective minimum spanning tree

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Algorithmic improvements on dynamic programming for the bi-objective {0,1} knapsack problem

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COMPUTATIONAL optimization AND APPLICATIONS 2013年第1期56卷 97-111页

作者： Figueira, Jose Rui Paquete, Luis Simoes, Marco Vanderpooten, Daniel Univ Tecn Lisboa CEG IST Ctr Management Studies Inst Super Tecn P-2780990 Porto Salvo Portugal Univ Coimbra CISUC Dept Informat Engn P-3030290 Coimbra Portugal Univ Paris 09 LAMSADE F-75775 Paris 16 France

This paper presents several methodological and algorithmic improvements over a state-of-the-art dynamic programming algorithm for solving the bi-objective {0,1} knapsack problem. The variants proposed make use of new definitions of lower and upper bounds, which allow a large number of states to be discarded. The computation of these bounds are based on the application of dichotomic search, definition of new bound sets, and bi-objective simplex algorithms to solve the relaxed problem. Although these new techniques are not of a common application for dynamic programming, we show that the best variants tested in this work can lead to an average improvement of 10 to 30 % in CPU-time and significant less memory usage than the original approach in a wide benchmark set of instances, even for the most difficult ones in the literature.

关键词： Bi-objective 0-1 knapsack problems multi-objective combinatorial optimization Bounds sets Dichotomic search Bi-objective simplex algorithm

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