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An Adaptive Evolutionary multi-objective Approach Based on Simulated Annealing

EVOLUTIONARY COMPUTATION 2011年第4期19卷 561-595页

作者： Li, H. Landa-Silva, D. Univ Nottingham Sch Comp Sci ASAP Res Grp Nottingham NG7 2RD England Xi An Jiao Tong Univ Sch Sci Xian Peoples R China

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

关键词： multi-objective combinatorial optimization Pareto optimality evolutionary multi-objective algorithms multi-objective simulated annealing adaptive weight vectors

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multi-criteria location planning for public facilities in tsunami-prone coastal areas

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OR SPECTRUM 2009年第3期31卷 651-678页

作者： Doerner, Karl F. Gutjahr, Walter J. Nolz, Pamela C. Univ Vienna Dept Business Adm A-1210 Vienna Austria Dept Stat & Decis Support Syst A-1010 Vienna Austria

We present a model for multi-objective decision analysis with respect to the location of public facilities as schools in areas near to coasts, taking risks of inundation by tsunamis into account. A mathematical programming formulation with three objective functions is given. The first objective function is a weighted mean of a minisum and a maximum coverage criterion. The second objective function expresses risk by possible tsunami events;for quantifying this risk, a statistical model for tsunami occurrences by Kaistrenko and Pinegina is applied. The third criterion represents costs. For the solution of the multi-objective optimization problem, we propose a heuristic approach based on the NSGA-II algorithm and compare it with a decomposition technique where the region under consideration is partitioned into smaller sub-regions, and the problem is solved for each separate subregion either exactly or heuristically. Both approaches are tested on two real-life instances from southern Sri Lanka.

关键词： multi-objective combinatorial optimization Locational analysis Genetic algorithm Disaster recovery planning

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Two-phase method and Lagrangian relaxation to solve the Bi-objective Set Covering Problem

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ANNALS OF OPERATIONS RESEARCH 2006年第1期147卷 23-41页

作者： Prins, Christian Prodhon, Caroline Calvo, Roberto Wolfler Univ Technol Troyes Inst Charles Delaunay F-10010 Troyes France Univ Technol Troyes ISTIT F-10010 Troyes France

This paper deals with the Bi-objective Set Covering Problem, which is a generalization of the well-known Set Covering Problem. The proposed approach is a two-phase heuristic method which has the particularity to be a constructive method using the primal-dual Lagrangian relaxation to solve single objective Set Covering problems. The results show that this algorithm finds several potentially supported and unsupported solutions. A comparison with an exact method (up to a medium size), shows that many Pareto-optimal solutions are retrieved and that the other solutions are well spread and close to the optimal ones. Moreover, the method developed compares favorably with the Pareto Memetic Algorithm proposed by Jaszkiewicz.

关键词： multi-objective combinatorial optimization Lagrangean relaxation set covering problem

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A multi-objective metaheuristic approach for the Transit Network Design Problem

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PUBLIC TRANSPORT 2009年第4期1卷 253-273页

作者： Mauttone, Antonio Urquhart, Maria E. Univ Republica Dept Operat Res J Herrera & Reissig 565 Piso 5 Montevideo Uruguay

We study the problem of the optimal design of routes and frequencies in urban public transit systems, the Transit Network Design Problem (TNDP), which is modeled as a multi-objective combinatorial optimization problem. A new heuristic based on the GRASP metaheuristic is proposed to solve the TNDP. As a multi-objective metaheuristic, it produces in a single run a set of non-dominated solutions representing different trade-off levels between the conflicting objectives of users and operators. Previous approaches have dealt with the multi-objective nature of the problem by weighting the different objectives into a single objective function. The case proposed by Mandl is used to show that the multi-objective metaheuristic is capable of producing a diverse set of solutions, which are compared with solutions obtained by other authors. We show that the proposed algorithm produces more non-dominated solutions than the Weighted Sum Method with the same computational effort, using the case of Mandl and another real test case.

关键词： Transit Network Design Problem multi-objective combinatorial optimization GRASP

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GASA Hybird Algorithm Applied in Airline Crew Rostering System

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Tsinghua Science and Technology 2007年第S1期12卷 255-259页

作者：张应辉饶云波周明天 Neusoft Institute of Information School of Computer Science and Engineering University of Electronic Science and Technology of China

Crew rostering system is a daily grind in the management of both corporation and enterprise. A fair and reasonable rostering method plays a very important role in the arousing worker’s enthusiasm and improving the work efficiency. This paper presents a method of building models for automatic crew rostering mode with computer and advancing the multi-objective optimum scheme. The method to build models for crew rostering system is also discussed. The question to crew rostering system model is solved by genetic algorithms and simulated annealing algorithms. Simulation results show the correctness of algorithms. The actual data of the airways have justified its reasonability and efficiency.

关键词： service system crew rostering system multi-objective combinatorial optimization simulated annealing algorithms genetic algorithms crew rostering system model

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Modelling beneficiaries' choice in disaster relief logistics

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ANNALS OF OPERATIONS RESEARCH 2017年第1期256卷 41-61页

作者： Burkart, Christian Nolz, Pamela C. Gutjahr, Walter J. WU Vienna Univ Econ & Business Inst Transport & Logist Management Welthandelspl 1 A-1020 Vienna Austria AIT Austrian Inst Technol Mobil Dept Dynam Transportat Syst Giefinggasse 2 A-1210 Vienna Austria Univ Vienna Dept Stat & Operat Res Oskar Morgenstern Pl 1 A-1090 Vienna Austria

This paper introduces the element of beneficiaries' choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the challenge where to establish distribution centers (DCs) for relief goods. For this purpose, two objectives are considered: the impact of the relief operations on the beneficiaries and the efficient use of monetary resources. The proposed multi-objective location-routing model minimizes unserved demand as well as cost for opening DCs and for routing relief goods. It anticipates the choice of beneficiaries to which DC to go (if at all), based on a model adopted from the literature on competitive location analysis. A mathematical programming formulation is presented. For small instances, the Pareto front can be determined exactly using an epsilon constraint method. For solving also realistic instances, an evolutionary algorithm has been implemented and evaluated. The algorithms are tested on real-world instances from Mozambique. The results show that when designing a distribution network, improvements can be achieved by taking the predicted behavior of beneficiaries into account.

关键词： multi-objective combinatorial optimization Disaster relief Competitive location model Evolutionary algorithm

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multi-objective optimization model and algorithm for hot rolling lot planning

Multi-objective optimization model and algorithm for hot rol...

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6th World Congress on Intelligent Control and Automation

作者： Ning, Shu-shi Wang, Wei Dalian Univ Technol Res Ctr Informat & Control Dalian 116024 Peoples R China

ISBN: (纸本)1424403316

A multi-objective combinatorial optimization model is formulated for hot rolling lot planning problem in the production scheduling of iron and steel enterprises and a new modified multi-objective genetic local search algorithm is designed to solve the model. The model can solve the problem more precisely than previous methods. The algorithm can provide the schedulers with more than one solution in order to help schedulers make further decisions. Simulation experiment using production data shows that the model and algorithm are effective.

关键词： hot rolling lot plan multi-objective combinatorial optimization multi-objective genetic local search

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A multi-objective Genetic Algorithm with Path Relinking for the p-Median Problem

A Multi-Objective Genetic Algorithm with Path Relinking for ...

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12th Ibero-American Conference on AI

作者： Arroyo, Jose E. C. dos Santos, Paula M. Soares, Michele S. Santos, Andre G. Univ Fed Vicosa Dept Informat Campus Univ UFV BR-3657000 Centro Vicosa MG Brazil

ISBN: (纸本)9783642169519

This paper considers the p-median problem that consists in finding p-locals from a set of m candidate locals to install facilities minimizing simultaneously two functions: the sum of the distances from each customer to its nearest facility and the sum of costs for opening facilities. Since this is a NP-Hard problem, heuristic algorithms are the most suitable for solving such a problem. To determine nondominated solutions, we propose a multi-objective genetic algorithm (MOGA) based on a nondominated sorting approach. The algorithm uses an efficient elitism strategy and an intensification operator based on the Path Relinking technique. To test the performance of the proposed MOGA, we develop a Mathematical Programming Algorithm, called epsilon-Constraint, that finds 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 nondominated frontier of the bi-objective problem efficiently.

关键词： Genetic Algorithms multi-objective combinatorial optimization p-Medians Path Relinking

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A multi-objective minimum matrix search algorithm applied to large-scale bi-objective TSP 5

A multi-objective minimum matrix search algorithm applied to...

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5th ACIS International Conference on Computational Science/Intelligence and Applied Informatics (CSII)

作者： Smith, Michael Manuel Chen, Yun Shiow Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan

ISBN: (纸本)9781538678756

The well-known NP-hard traveling salesman problem (TSP) primarily considers distance as its single objective. However, applications modeled from real world systems repeatedly involve more than one objective giving rise to multi-objective optimization. Fusing ideas of dimension reduction, decomposition approaches, and genetic algorithms, this paper presents a multi-objective minimum matrix search algorithm (MOMMS) for the heuristic resolution of the bi-objective TSP (bTSP). The MOMMS uses dimension reduction to obtain a reduce matrix network that is used to obtain or to approximate the set of efficient solutions. The reduce matrix network aids in the decomposition of a multi-objective combinatorial optimization (MOCO) problem into a single objective combinatorial optimization problem. Moreover, using the reduce matrix network MOMMS introduces a population generator that creates an initial population composed of an approximation to the extreme supported efficient solutions. The MOMMS does not use any numerical parameter. Also, MOMMS uses family competitive metamorphosis and short-term memory selection to maintain population diversity in MOCO problems. The proposed algorithm showed respectable results in testing on well-known benchmark problems of the bTSP. Comparisons are performed with the results of state-of-the-art algorithms from the literature. Moreover, the MOMMS is tested on large-scale instances of the bTSP. The computational study shows that the proposed algorithm is able to solve large-scale instances in reasonable time. Therefore, the MOMMS is a competitive tool for solving the bTSP.

关键词： Bi-objective traveling salesman problem multi-objective combinatorial optimization dimension reduction decomposition approaches genetic algorithms

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Min cost improvement and max gain stability in multicriteria sorting methods on combinatorial domains

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JOURNAL OF multi-CRITERIA DECISION ANALYSIS 2021年第3-4期28卷 170-184页

作者： Benabbou, Nawal Martin, Hugo Perny, Patrice Sorbonne Univ CNRS DECISION Team Lab Informat Paris Paris France

Various multicriteria sorting methods have been proposed in the literature to assign the feasible alternatives into predefined categories. We consider here problems involving a set of totally ordered categories representing different achievement levels in the satisfaction of criteria. As in many existing methods, the assignment rule of an alternative to a category is based on the comparison of its performance vector to reference profiles defining lower bounds of the categories. Within this standard setting we address a new problem that consists in finding how to modify a given solution, within a combinatorial set of alternatives, to upgrade it in the upper category (or higher) at minimum cost. We also consider the problem of identifying the sequence of solutions that minimize the total cost while satisfying some budget constraint at every step, and the problem of determining how to modify the current solution to save money while staying in the same category. We first propose a general approach based on mixed integer (linear or quadratic) programming to solve these problems. Then, we implement this approach on various multiobjective combinatorial problems, such as multi-agent assignment problems and multiobjective knapsack problems. Numerical tests are provided to establish the feasibility of the approach on instances of different sizes.

关键词： max gain stability min cost improvement multicriteria sorting method multi-objective combinatorial optimization

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