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IEEE Congress on Evolutionary Computation

作者： Acan, A Ünveren, A Eastern Mediterranean Univ Dept Comp Engn TRNC Gazimagusa Turkey

ISBN: (纸本)0780393635

multiobjective evolutionary optimization has been demonstrated to be an efficient method for some difficult multiobjective optimization problems;particularly the quadratic assignment problem which is a provably difficult NP-complete problem with a multitude of real-world applications. This paper introduces the use of a segment-based external memory in evolutionary multiobjective optimization. In principle, variable-size solution segments taken from a number of previously promising solutions are stored in an external memory whose elements are used in the construction of new solutions. In the construction of a solution, a solution segment is retrieved from the external memory and used in the construction of complete solutions through evolutionary recombination operators. The aim is to provide further intensification around promising solutions without weakening the exploration capabilities. Different instances of the multiobjective quadratic assignment problem are used for performance evaluations and, almost in all trials, the proposed external memory strategy provided significantly better results than the multiobjective genetic algorithm (MOGA).

关键词： computational complexity evolutionary computation optimisation evolutionary multiobjective optimization evolutionary recombination operators multiobjective genetic algorithm multiobjective quadratic assignment problem segment based external memory variab

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A multiobjective genetic algorithm for job shop scheduling

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PRODUCTION PLANNING & CONTROL 2001年第8期12卷 764-774页

作者： Ponnambalam, SG Ramkumar, V Jawahar, N Reg Engn Coll Dept Prod Engn Tiruchirappalli 620015 India Thiagarajar Coll Engn Dept Engn Mech Madurai 625015 Tamil Nadu India Tata Consultance Serv Chennai India

In this paper, a Multi Objective genetic algorithm (MOGA) is proposed to derive the optimal machine-wise priority dispatching rules (pdrs) to resolve the conflict among the contending jobs in the Giffler and Thompson (GT) procedure applied for job shop problems. The performance criterion considered is the weighed sum of the multiple objectives minimization of makespan, minimization of total idle time of machines and minimization of total tardiness. The weights assigned for combining the objectives into a scalar fitness function are not constant. They are specified randomly for each evaluation. This in turn leads to the multidirectional search in the proposed MOGA, which in turn mitigates the solution being entrapped in local minima. The applicability and usefulness of the proposed methodology for the scheduling of job shops is illustrated with 28 benchmark problems available in the open literature.

关键词： job shop scheduling multiobjective genetic algorithm

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Simultaneous halftone image generation with improved multiobjective genetic algorithm

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2001年第8期E84A卷 1869-1882页

作者： Aguirre, H Tanaka, K Sugimura, T Oshita, S Shinshu Univ Fac Engn Nagano 3808553 Japan

A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images, Recently, the two major drawbacks of this conventional halftoning technique with CAs, i.e. it uses a substantial amount of computer memory and processing time, have been overcome by using an improved CA (CA-SR-NI) that applies genetic operators in parallel putting them in a cooperative-competitive stand with each other. The halftoning problem is a true multiobjective optimization problem. However, so far, tho CA based halftoning techniques have treated the problem as a single objective optimization problem, In this work. the improved CA-SRM is extended to a multiobjective optimization CA to simultaneously generate halftone images with various combinations of gray level precision and spatial resolution. Simulation results verify that the proposed scheme can effectively generate several high quality images simultaneously in a single run reducing even further the overall processing time.

关键词： multiobjective genetic algorithm multiobjective optimization image halftoning problem cooperative-competitive genetic operators

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genetic-algorithm-based real-time task scheduling with multiple goals

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JOURNAL OF SYSTEMS AND SOFTWARE 2004年第3期71卷 245-258页

作者： Oh, J Wu, C Seoul Natl Univ Sch Engn & Comp Sci SE Lab Seoul 151742 South Korea

This paper presents and evaluates a new method for real-time task scheduling in multiprocessor systems. Its objectives are to minimize the number of processors required and the total tardiness of tasks. The minimization is carried out through a multiobjective genetic algorithm (GA), because the problem has non-commensurable and competing objectives to be optimized. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific GA, the performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs. Also shown is the impact of the sparsity of a task graph on the performance of-our algorithm. (C) 2002 Elsevier Inc. All rights reserved.

关键词： scheduling real-time system deadline total tardiness multiobjective genetic algorithm

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Incremental multiple objective genetic algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第3期34卷 1325-1334页

作者： Chen, Q Guan, SU Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore

This paper presents a new genetic algorithm approach to multiobjective optimization problems-incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.

关键词： incremental problem solving multiobjective genetic algorithm multiobjective problems multiobjective optimization vector optimization

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Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2004年第11期18卷 665-682页

作者： Cottrell, SJ Gillet, VJ Taylor, R Wilton, DJ Univ Sheffield Dept Informat Studies Sheffield S1 4DP S Yorkshire England Cambridge Crystallog Data Ctr Cambridge CB2 1EZ England

Pharmacophore methods provide a way of establishing a structure--activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation.

关键词： alignment conformational analysis evolutionary algorithms MOGA molecular overlay multiobjective genetic algorithm multiobjective optimisation pharmacophore identification

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A multiobjective optimization method for strategic sourcing and inventory replenishment

A multiobjective optimization method for strategic sourcing ...

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IEEE International Conference on Robotics and Automation

作者： Ding, HW Benyoucef, L Xie, XL French Natl Inst Res Comp Sci & Control INRIA F-57000 Metz France

ISBN: (纸本)0780382323

A simulation-based multiobjective optimization method is proposed in this paper for joint decision-making on strategic sourcing and inventory replenishment. More specifically, a multiobjective genetic algorithm is developed to determine the optimal supplier portfolio and inventory control parameters in order to reach best compromise of two conflicting criteria: costs and demand fill-rate. Discrete-event simulation is used to provide faithful evaluation of these two criteria. Numerical results on a variant of a real case study are presented.

关键词： multiobjective genetic algorithm simulation supplier selection inventory replenishment

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Designing Combinatorial Libraries Optimized on Multiple Objectives

Designing Combinatorial Libraries Optimized on Multiple Obje...

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作者： Valerie J. Gillet

The recent emphasis in combinatorial library design has shifted from the design of very large diverse libraries to the design of smaller more focused libraries. Typically the aim is to incorporate as much knowledge into the design as possible. This knowledge may relate to the target protein itself or may be derived from known active and inactive compounds. Other factors should also be taken into account, such as the cost of the library and the physicochemical properties of the compounds that are contained within the library. Thus, library design is a multiobjective optimization problem. Most approaches to optimizing multiple objectives are based on aggregation methods whereby the objectives are assigned relative weights and are combined into a single fitness function. A more recent approach involves the use of a multiobjective genetic algorithm in which the individual objectives are handled independently without the need to assign weights. The result is a family of solutions each of which represents a different compromise in the objectives. Thus, the library designer is able to make an informed choice on an appropriate compromise solution. less

关键词： Simulated Annealing Monte Carlo Simulations Combinatorial libraries combinatorial synthesis genetic algorithms molecular descriptors computational filtering druglike library enumeration MOGA multiobjective genetic algorithm multiobjective optimization

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multiobjective genetic algorithm approach for a dual-feed circular polarised patch antenna design

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ELECTRONICS LETTERS 2000年第12期36卷 1005-1006页

作者： Aljibouri, B Lim, EG Evans, H Sambell, A Northumbria Univ Commun Syst Res Grp Newcastle Upon Tyne NE1 8ST Tyne & Wear England

A design procedure for a dual feed network is presented to produce a circular polarised matched antenna involving eight design parameters with associated constraints. Determination of such design parameters has been made possible by utilising a multiobjective genetic algorithm (MGA) approach. The conditions For circular polarisation and impedance matching were the objective functions employed in the MGA. The associated constraints were the lengths and characteristic impedance values of the feed network. The return loss and axial ratio for a 5.8GHz antenna were investigated and good agreement was obtained between simulated and practical measurements.

关键词： characteristic impedance values dual-feed patch antenna design objective functions antenna theory design parameters Antenna theory circular polarisation equivalent circuits Single antennas circular polarised patch antenna design Antenna accessories axial ratio impedance matching antenna feeds return loss genetic algorithms dual feed network microstrip antennas circular polarised matched antenna multiobjective genetic algorithm design procedure Optimisation techniques 5.8 GHz electric impedance electromagnetic wave polarisation

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A multi-objective genetic algorithm for solving assembly line balancing problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2000年第5期16卷 341-352页

作者： Ponnambalam, SG Aravindan, P Naidu, GM Reg Engn Coll Dept Prod Engn Tiruchirappalli 620015 India PSG Coll Technol Dept Mech Engn Coimbatore 641004 Tamil Nadu India

In this paper, a multi-objective genetic algorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer. The developed genetic algorithm is compared with six popular heuristic algorithms, namely, ranked positional weight, Kilbridge and West, Moodie and Young, Hoffmann precedence matrix, immediate update first fit, and rank and assign heuristic methods. For comparative evaluation, 20 networks are collected from open literature, and are used with five different cycle times. All the six heuristics and the genetic algorithm are coded in C++ language. It is found that the proposed genetic algorithm performs better in all the performance measures than the heuristics. However, the execution time for the GA is longer, because the GA searches for global optimal solutions with more iterations.

关键词： assembly line balancing heuristic rules multiobjective genetic algorithm

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