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Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

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COMPUTERS & INDUSTRIAL ENGINEERING 2008年第3期54卷 539-569页

作者： Dietz, A. Azzaro-Pantel, C. Pibouleau, L. Domenech, S. CNRS INPT UPS UMR 5503 Lab Genie Chim F-31106 Toulouse 1 France

This work deals with multiobjective optimization problems using genetic algorithms (GA). A multiobjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto, optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： multiobjective genetic algorithm multiobjective optimization batch plant design Pareto sort procedure

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An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time

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JOURNAL OF INTELLIGENT MANUFACTURING 2011年第3期22卷 367-378页

作者： Zhang, Wenqiang Gen, Mitsuo Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

Mixed-model assembly lines (mALs) are becoming more and more important by producing different models of the same product on an assembly line. How to calculate the cycle time based on demand of different models also making problem more difficult. According to different work experiences and skill level, the processing time of a given task and the operating costs such as wages differ among workers. Appointing the proper worker to the proper station and assigning the suitable task to the suitable station in order to decrease the cycle time, increase the line efficiency, and reduce the total cost make the problem more complex. This paper proposes a new concept for calculating the cycle time based on demand ratio of each model and another one for calculating the human resource cost. A generalized Pareto-based scale-independent fitness function genetic algorithm (gp-siffGA) is described for solving mixed-model assembly lines balancing (mALB) problems to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. The gp-siffGA uses Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. Comparisons with existing multiobjective genetic algorithms demonstrate that our approach efficiently solves mALB problems.

关键词： Mixed-model assembly line balancing Worker allocation multiobjective genetic algorithm Demand ratio-based cycle time Pareto dominance relationship

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A Hybrid multiobjective genetic algorithm on Optimizing Aircraft Schedule Recovery Problems under Short-Time Response

A Hybrid Multiobjective Genetic Algorithm on Optimizing Airc...

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IEEE International Conference on System, Man, and Cybernetic

作者： Chen, Chiu-Hung Liu, Tung-Kuan Chou, Jyh-Horng Tsai, Jinn-Tsong Ho, Wen-Hsien Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung Taiwan Natl Pingtung Univ Educ Dept Comp Sci Pingtung Taiwan Kaohsiung Med Univ Dept Med Informat Management Kaohsiung Taiwan

ISBN: (纸本)9781424423835

This article presents a hybrid multiobjectivc genetic algorithm to aid the tracking of the daily aircraft schedule recovery problem under disturbance events such as severe weather and mechanical problems. The proposed algorithm extends from the original method of inequality-based multiobjective genetic algorithm (MMGA) and utilizes an adaptive evaluated vector (AEV) to co-work with MMGA efficiently when maintaining the Pareto set of recovered schedules in the evolutionary population. Two main goals would be presented: One is to provide a multi-objective solution to the recovery problem and the other is to address the performance requirement on the recovery approach. A simulated disturbance experiment on the practical aircraft schedule is made to validate the recovery results under the expected short-time period.

关键词： multiobjective genetic algorithm Aircraft Schedule Recovery Pareto Set

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AN APPROACH TO COMPUTER-AIDED PROCESS PLANNING IN DISTRIBUTED MANUFACTURING USING multiobjective genetic algorithm

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38th International Conference on Computers and Industrial Engineering

作者： Zhang, Wenqiang Gen, Mitsuo Lin, Lin Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9787121074370

In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those available factories and different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a multiobjective genetic algorithm (moGA) using a random key-based representation method, which, according to prescribed criteria such as minimizing processing time, minimizing production cost, minimizing transportation time and cost between factories, could swiftly search for the optimal process plan in distributed manufacturing systems. The process plan is decided by operations sequence, factory selection, machine selection, tool selection and tool access direction selection. By applying the moGA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans.

关键词： Computer-Aided Process Planning Distributed Manufacturing multiobjective genetic algorithm Operation Sequence

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A multiobjective genetic algorithm for Assembly Line Balancing Problem with Worker Allocation

A Multiobjective Genetic Algorithm for Assembly Line Balanci...

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IEEE International Conference on System, Man, and Cybernetic

作者： Zhang, Wenqiang Gen, Mitsuo Lin, Lin Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781424423835

The Assembly Line Balancing (ALB) problem is a well-known manufacturing optimization problem, which determines the assignment of various tasks to an ordered sequence of stations, while optimizing one or more objectives without violating restrictions Imposed on the line. As genetic algorithms (GAs) have established themselves as a useful optimization technique in the manufacturing field, the application of GAs to ALB problem has expanded a lot. This paper describes a generalized Pareto-based scale-independent fitness function (gp-siffGA) for solving ALB problem with worker allocation (ALB-wa) to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. For this appraoch, first a random key-based representation method adapting the GA was proposed. Following, advanced genetic operators adapted to the specific chromosome structure and the characteristics of the ALB-wa problem were used. Moreover, Pareto dominance relationship was used to solve the ALB-wa problem without using relative preferences of multiple objectives. Finally, the performance of proposed method was validated through numerical experiments. The results indicated that the proposed approach improved the quality of solutions more than the other existing GA approaches.

关键词： Assembly line balancing generalized Pareto-based scale-independent fitness function multiobjective genetic algorithm random key

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Inserting Co-evolution Information from Contact Maps into a multiobjective genetic algorithm for Protein Structure Prediction

Inserting Co-evolution Information from Contact Maps into a ...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Rocha, Gregorio K. dos Santos, Karina B. Angelo, Jaqueline S. Custodio, Fabio L. Barbosa, Helio J. C. Dardenne, Laurent E. MCTIC LNCC Petropolis RJ Brazil Univ Fed Juiz De Fora Juiz De Fora MG Brazil

ISBN: (纸本)9781509060177

Protein structure prediction (PSP) can be described as a multiobjective optimization (MO) problem since the energy function involves potentially conflicting terms to be simultaneously optimized. During the last three CASP editions (10th, 11th, and 12th), promising results were achieved with the introduction of co-evolution information, in the form of residues contact maps, in methodologies for PSP. In this paper, a residue-residue contact map potential is introduced into the evaluation function of the GAPF program, and it is optimized using a MO strategy. The Aggregation Tree (AT) method is applied to group in separated objectives the energetic potentials that compose the GAPF's evaluation function. The results are compared with those obtained from two consolidated PSP methods, QUARK and MEAMT.

关键词： Protein Structure Prediction multiobjective genetic algorithm Residue-Residue Contact Maps Aggregation Tree

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OPTIMIZATION OF SHORT-HAUL AIRLINE CREW PAIRING PROBLEMS USING A multiobjective genetic algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2010年第9期6卷 3943-3959页

作者： Chen, Chiu-Hung Chou, Ta-Yuan Liu, Tung-Kuan Chou, Jyh-Horng Lee, Chung-Nan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan

Airline crew pairing problems involve optimizing an overall evaluation function containing various conflicting objectives and constraints originating from cost and safety considerations. Classical approaches based on set partitioning or set covering methods separate the solution into two phases, pairing generation and pairing optimization, and evaluate the cost by a weighted-sum of objective values. This paper proposes a new multiobjective evolutionary approach to improve the classical solution flow by integrating the two-phase steps as a single step and reasoning the multiple practical objectives simultaneously. Furthermore, this paper also examines real-life daily pairing problems in a Taiwanese short-haul airline as case studies. Compared to man-made pairing plans, the positive experimental results demonstrate the more appropriate and effective crew pairing plans explored according to practical considerations. These considerations include objectives such as duty connection, transition time, layover, pairing number, aircraft changing limes, flying time, and duty period.

关键词： Crew pairing multiobjective genetic algorithm Short-haul airline

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Analysis of outrigger numbers and locations in outrigger braced structures using a multiobjective genetic algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2018年第1期27卷 1-1页

作者： Chen, Yue Zhang, Zhenya Ningbo Univ Technol Sch Civil & Transportat Engn Ningbo 315106 Zhejiang Peoples R China

Due to its advantages, the outrigger braced system has been employed in high-rise structures for the last 3 decades. It is evident that the numbers and locations of outriggers in this system have a crucial impact on the performance of high-rise buildings. In this paper, a multiobjective genetic algorithm (MGA) is applied to an existing mathematical model of outrigger braced structures and a practical project to achieve Pareto optimal solutions, which treat the top drift and core base moment of a high-rise building as 2 trade-off objective functions. MATLAB was employed to explore a multiobjective automatic optimization procedure for the optimal design of outrigger numbers and locations under wind load. In this research, various schemes for the preliminary stages of design can be obtained using MGA. This allows designers and clients easily to compare the performance of structural systems with different numbers of outriggers in different locations. In addition, design results based on MGA offer many other benefits, such as diversity, flexible options for designers, and active client participation.

关键词： high-rise building multiobjective genetic algorithm optimum design outrigger braced system Pareto optimal solution wind load

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Optimal design of plasmonic waveguide using multiobjective genetic algorithm

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OPTICAL ENGINEERING 2016年第1期55卷 017103-017103页

作者： Jung, Jaehoon Dankook Univ Dept Elect & Elect Engn 152 Jukjeon Ro Yongin 16890 Gyeonggi Do South Korea

An approach for multiobjective optimal design of a plasmonic waveguide is presented. We use a multiobjective extension of a genetic algorithm to find the Pareto-optimal geometries. The design variables are the geometrical parameters of the waveguide. The objective functions are chosen as the figure of merit defined as the ratio between the propagation distance and effective mode size and the normalized coupling length between adjacent waveguides at the telecom wavelength of 1550 nm. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： surface plasmons guided waves waveguides multiobjective genetic algorithm

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Sit-to-Stand Motion Analysis Using multiobjective genetic algorithm Based on Musculoskeletal Model Simulation

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IEEJ JOURNAL OF INDUSTRY APPLICATIONS 2016年第3期5卷 236-244页

作者： Yokota, Hiroki Ohshima, Shigemichi Mizuno, Naoki Nagoya Inst Technol Shouwa Ku Gokiso Cho Nagoya Aichi 4668555 Japan Meijo Univ Tempaku Ku 1-501 Shiogamaguchi Nagoya Aichi 4688502 Japan

One of the most vital skills of daily life is the ability to stand from a sitting position. This study aims to use musculoskeletal model simulation to create an optimal sit-to-stand motion without causing stress to the knee joints while activating the weak muscles. Numerical optimization was performed using the musculoskeletal model by applying the multiobjective genetic algorithm. The numerical optimization results indicate that by placing the feet under the chair prior to standing (not bending the upper body visibly forward) during the sit-to-stand movement, it is possible to reduce stress on the knee joints and muscles. To validate this optimized sit-to-stand motion, we performed two experiments. First, we calculated a correlation value between simulation and experimental results relative to muscle activity, and verified that there is a significant correlation. Next, two types of sit-to-stand movements performed by five healthy male subjects were analyzed: spontaneous sitting-to-standing and imitated sitting-to-standing using the optimized motion. The experimental results confirm that the imitated movement reduces the knee joint's maximum torque.

关键词： sit-to-stand motion optimization musculoskeletal model simulation multiobjective genetic algorithm

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