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A note on the ε-indicator subset selection

THEORETICAL COMPUTER SCIENCE 2013年 499卷 113-116页

作者： Vaz, Daniel Paquete, Luis Ponte, Anibal Univ Coimbra Dept Informat Engn CISUC P-3000 Coimbra Portugal

The epsilon-indicator subset selection selects a subset of a nondominated point set that is as close as possible to a reference point set with respect to the epsilon-indicator. This selection procedure is used by population-based heuristic approaches for multiobjective optimization problems. Given that this procedure is called very often during the run of the heuristic approach, efficient ways of computing the optimal subset are strongly required. In this note, we give a correctness proof of the E-indicator subset selection algorithm proposed by Ponte et al. (2012) [1] for the bidimensional case as well as several algorithmic improvements in terms of time complexity. Extensions to larger dimension are also discussed. (C) 2013 Elsevier B.V. All rights reserved.

关键词： multiobjective combinatorial optimization Subset selection epsilon-Indicator

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Speed-up techniques for solving large-scale biobjective TSP

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COMPUTERS & OPERATIONS RESEARCH 2010年第3期37卷 521-533页

作者： Lust, T. Jaszkiewicz, A. Lab Math & Operat Res Fac Polytech Mons B-7000 Mons Belgium Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

In this paper, we present the Two-Phase Pareto Local Search (2PPLS) method with speed-up techniques for the heuristic resolution of the biobjective traveling salesman problem. The 2PPLS method is a state-of-the-art method for this problem. However, because of its running time that strongly grows with the instances size, the method can be hardly applied to instances with more than 200 cities. We thus adapt some speed-up techniques used in single-objective optimization to the biobjective case. The proposed method is able to solve instances with up to 1000 cities in a reasonable time with no, or very small, reduction of the quality of the generated approximations. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multiobjective combinatorial optimization Hybrid metaheuristics TSP Local search Speed-up techniques

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Exact hybrid algorithms for solving a bi-objective vehicle routing problem

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2012年第1期20卷 19-43页

作者： Reiter, Peter Gutjahr, Walter J. Univ Vienna Dept Stat & Decis Support Syst A-1010 Vienna Austria

The paper investigates a capacitated vehicle routing problem with two objectives: (1) minimization of total travel cost and (2) minimization of the length of the longest route. We present algorithmic variants for the exact determination of the Pareto-optimal solutions of this bi-objective problem. Our approach is based on the adaptive epsilon-constraint method. For solving the resulting single-objective subproblems, we apply a branch-and-cut technique, using (among others) a novel implementation of Held-Karp-type bounds. Incumbent solutions are generated by means of a single-objective genetic algorithm and, alternatively, by the multi-objective NSGA-II algorithm. Experimental results for a benchmark of 54 test instances from the TSPLIB are reported.

关键词： Capacitated vehicle routing problem Distance constraints multiobjective combinatorial optimization Branch-and-cut Genetic algorithms NSGA-II

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Synthesis of quantitative and qualitative evidence for accident analysis in risk-based highway planning

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ACCIDENT ANALYSIS AND PREVENTION 2006年第5期38卷 925-935页

作者： Lambert, James H. Peterson, Kenneth D. Joshi, Nilesh N. Univ Virginia Ctr Risk Management Syst Dept Syst & Informat Engn Charlottesville VA 22904 USA

Accident analysis involves the use of both quantitative and qualitative data in decision-making. The aim of this paper is to demonstrate the synthesis of relevant quantitative and qualitative evidence for accident analysis and for planning a large and diverse portfolio of highway investment projects. The proposed analysis and visualization techniques along with traditional mathematical modeling serve as an aid to planners, engineers, and the public in comparing the benefits of current and proposed improvement projects. The analysis uses data on crash rates, average daily traffic, cost estimates from highway agency databases, and project portfolios for regions and localities. It also utilizes up to two motivations out of seven that are outlined in the Transportation Equity Act for the 21st Century (TEA-21). Three case studies demonstrate the risk-based approach to accident analysis for short- and long-range transportation plans. The approach is adaptable to other topics in accident analysis and prevention that involve the use of quantitative and qualitative evidence, risk analysis, and multi-criteria decision-making for project portfolio selection. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： multi-criteria decision-making accident analysis crash rate transportation planning multiobjective combinatorial optimization

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Strategic technology planning in hospital management

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OR SPECTRUM 2003年第2期25卷 161-182页

作者： Focke, A Stummer, C Univ Vienna Sch Business Econ & Comp Sci Dept Innovat & Technol Management A-1210 Vienna Austria

Hospital managers regularly are confronted with their patients' demand for increased service performance on the one hand and tight budgets on the other. As medical technologies influence both the costs and the service spectrum, strategic technology planning is among the critical tasks in hospital management. This paper addresses the corresponding problem of selecting the "best" portfolio of medical devices and proposes an appropriate decision support system. It starts with a multiobjective mathematical programming model that determines the solution space of all efficient portfolios of medical technology investments. The members of the planning committee with their diverging interests then are supported by their interactive exploration of that space until they jointly reach a satisfying portfolio and are,not willing to make any further compromises between its objective levels. As one of its salient features the system does not require a priori preference information.

关键词： strategic hospital management medical technology planning portfolio selection multiobjective combinatorial optimization interactive decision support

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Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection

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ANNALS OF OPERATIONS RESEARCH 2004年第1-4期131卷 79-99页

作者： Doerner, K Gutjahr, WJ Hartl, RF Strauss, C Stummer, C Univ Vienna Dept Management Sci A-1210 Vienna Austria Univ Vienna Dept Stat & Decis Support Syst A-1010 Vienna Austria

Selecting the "best" project portfolio out of a given set of investment proposals is a common and often critical management issue. Decision-makers must regularly consider multiple objectives and often have little a priori preference information available to them. Given these contraints, they can improve their chances of achieving success by following a two-phase procedure that first determines the solution space of all efficient (i.e., Pareto-optimal) portfolios and then allows them to interactively explore that space. However, the task of determining the solution space is not trivial: brute-force complete enumeration only works for small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. Meta-heuristics provide a useful compromise between the amount of computation time necessary and the quality of the approximated solution space. This paper introduces Pareto Ant Colony optimization as an especially effective meta-heuristic for solving the portfolio selection problem and compares its performance to other heuristic approaches (i.e., Pareto Simulated Annealing and the Non-Dominated Sorting Genetic Algorithm) by means of computational experiments with random instances. Furthermore, we provide a numerical example based on real world data.

关键词： ant colony optimization simulated annealing genetic algorithms portfolio selection multiobjective combinatorial optimization

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Effects of the Use of Non-Geometric Binary Crossover on Evolutionary multiobjective optimization 07

Effects of the Use of Non-Geometric Binary Crossover on Evol...

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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Ishibuchi, Hisao Nojima, Yusuke Tsukamoto, Noritaka Ohara, Ken Osaka Prefecture Univ Grad Sch Engn Naka Ku Osaka 5998531 Japan

ISBN: (纸本)9781595936974

In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point and uniform) tend to decrease the diversity of solutions in EMO algorithms while they improve the convergence to the Pareto front. This is because Such a crossover operator, which is called geometric crossover, always generates ail offspring in the segment between its two parents under the Hamming distance in the genotype space. That is, the sum of the distances from the generated offspring to its two parents is always equal to the distance between the parents. In this paper, first we propose a non-geometric binary crossover operator to generate ail offspring outside the segment between its parents. Next we examine the effect of the use of non-geometric binary crossover oil single-objective genetic algorithms. Experimental results show that non-geometric binary crossover improves their search ability. Then we examine its effect on EMO algorithms. Experimental results show that non-geometric binary crossover drastically increases the diversity Of Solutions while it slightly degrades their convergence to the Pareto front. As a result, some performance measures Such as hypervolume are clearly improved.

关键词： Non-geometric crossover operators evolutionary multiobjective optimization (EMO) algorithms multiobjective combinatorial optimization diversity preserving balance between diversity and convergence

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Guided Pareto Local Search based Frameworks for Biobjective optimization

Guided Pareto Local Search based Frameworks for Biobjective ...

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2010 IEEE World Congress on Computational Intelligence

作者： Alsheddy, Abdullah Tsang, Edward E. P. K. Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781424481262

Guided Pareto Local Search (GPLS) is an extension to the Guided Local Search algorithm to contain multiobjective combinatorial optimization. GPLS is shown to improve the convergence of the underlying Pareto local search algorithms. This paper demonstrates the potential of GPLS to be an effective searching technique that can be a central part of a multi-phase or hybrid frameworks. To confirm this, two simple frameworks based on GPLS are proposed: iGPLS and mGPLS. Both frameworks only require an initial set of diverse solutions. While GPLS starts from a randomly (or heuristically) generated solution, iGPLS starts with the initial diverse solution set. On the other hand, mGPLS is a parallel version of GPLS, in which each GPLS run starts independently from a solution in the initial set. The application of these frameworks to the biobjective 0/1 knapsack problem reveals the effectiveness of the GPLS based frameworks, demonstrated by achieving state-of-the-art results.

关键词： Pareto optimisation biobjective 0/1 knapsack problem biobjective optimization combinatorial mathematics convergence convergence guided Pareto local search knapsack problems multiobjective combinatorial optimization search problems searching technique

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COORDINATION AND DIVERSIFICATION OF INVESTMENTS IN MULTIMODAL TRANSPORTATION

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PUBLIC WORKS MANAGEMENT & POLICY 2007年第4期11卷 250-265页

作者： Lambert, James H. Joshi, Nilesh N. Peterson, Kenneth D. Wadie, Shadi M. Univ Virginia Ctr Risk Management Engn Syst & Res Charlottesville VA 22903 USA Univ Virginia Syst & Informat Engn Charlottesville VA 22903 USA Univ Virginia Ctr Risk Management Engn Syst Charlottesville VA 22903 USA

Across the nation, there are opportunities to improve multimodal coordination and prioritization of investments among transportation agencies. This article develops a method to coordinate and diversify transportation investments across travel modes and qualitative goals. The method helps decision makers to identify and prioritize investments for multimodal corridors, which involve large-scale coordination of transportation projects across modes. The article describes the developed method in two parts: prioritizing multimodal corridors at a macro level of analysis and coordinating modal projects at a micro level. The benefits of the method include (a) identification of efficient investment alternatives to meet travel demands when considering multiple modes relative to only single modes, (b) direction of funding to the multimodal corridors and modal projects where the needs are most urgent, and (c) increased transparency and accountability of multimodal agencies for funding that can be allocated across multiple modes. The article addresses multimodal transportation planning efforts with systematic, evidence-based approaches to coordinating long-range investments.

关键词： multiobjective combinatorial optimization transportation planning multimodal corridor multiobjective decision making systems integration diversification

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A metaheuristic evolutionary method for electrical performance estimation of distribution networks

A metaheuristic evolutionary method for electrical performan...

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IEEE-Power-Engineering-Society General Meeting

作者： Hashimoto, K Kagan, N Santoso, S Sao Paulo State Energy Regulatory Commiss CSPE Sao Paulo Brazil

ISBN: (纸本)078039156X

This paper proposes a new probabilistic method to evaluate power distribution electrical performance. Electrical performance is assumed to be the evaluation of network congestion parameters, losses and voltage level. The proposed method is suitable for off-line applications and it considers steady state performance only. The electrical performance estimation is formulated as a multiobjective optimization problem where the objective functions correspond to an evaluation of occurrence probability, and also correspond to a proximity evaluation of calculated voltage levels with values obtained by measurement (goal programming). Load profile values are discretized according to its occurrence probabilities (universal discrete probability density function), so that formulation results in a multiobjective combinatorial optimization. The optimization formulation considers feeder output power and voltage level measurement in any point of the network as restriction parameters. Network reduction procedures to substantially reduce Decision Domain and network expansion procedures to rebuild it are proposed. Specific heuristics - applicable to radial networks - were also developed to generate a-priori feasible candidate solutions combining unbalanced and diversification load values. A metaheuristic evolutionary method is proposed and applied in order to improve feasible solutions and adequately apply the heuristics. Feasible solutions are classified according to Dominance Ranking algorithm, so that electrical performance is obtained for each dominance rank frontier.

关键词： electrical performance estimation. multiobjective combinatorial optimization evolutionary method metatieuristics

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