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multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables

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IET RENEWABLE POWER GENERATION 2010年第5期4卷 404-419页

作者： Katsigiannis, Y. A. Georgilakis, P. S. Karapidakis, E. S. Tech Univ Crete Dept Prod Engn & Management GR-73100 Khania Greece Natl Tech Univ Athens Sch Elect & Comp Engn GR-15780 Athens Greece Technol Educ Inst Crete Dept Environm & Nat Resources GR-73133 Khania Greece

The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria.

关键词： Secondary cells secondary cells small autonomous hybrid power systems Pareto set optimisation power sources GHG emissions disposal-recycling Pollution detection and control Optimisation techniques nonlinear programming sorting total greenhouse gas emissions raw material extraction nondominated sorting genetic algorithm Combinatorial mathematics Other energy storage hydrogen storage multiobjective genetic algorithm Power system management, operation and economics optimum economic criteria air pollution control hybrid power systems nonlinear combinatorial multiobjective optimisation problem lead-acid batteries system cost of energy minimisation life cycle analysis environmental criteria power system economics genetic algorithms energy storage

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multiobjective genetic algorithm-Based Fuzzy Clustering of Categorical Attributes

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2009年第5期13卷 991-1005页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Recently, the problem of clustering categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, has been gaining significant attention from researchers. With the growing demand for categorical data clustering, a few clustering algorithms with focus on categorical data have recently been developed. However, most of these methods attempt to optimize a single measure of the clustering goodness. Often, such a single measure may not be appropriate for different kinds of datasets. Thus, consideration of multiple, often conflicting, objectives appears to be natural for this problem. Although we have previously addressed the problem of multiobjective fuzzy clustering for continuous data, these algorithms cannot be applied for categorical data where the cluster means are not defined. Motivated by this, in this paper a multiobjective genetic algorithm-based approach for fuzzy clustering of categorical data is proposed that encodes the cluster modes and simultaneously optimizes fuzzy compactness and fuzzy separation of the clusters. Moreover, a novel method for obtaining the final clustering solution from the set of resultant Pareto-optimal solutions in proposed. This is based on majority voting among Pareto front solutions followed by k-nn classification. The performance of the proposed fuzzy categorical data-clustering techniques has been compared with that of some other widely used algorithms, both quantitatively and qualitatively. For this purpose, various synthetic and real-life categorical datasets have been considered. Also, a statistical significance test has been conducted to establish the significant superiority of the proposed multiobjective approach.

关键词： Categorical attributes fuzzy clustering multiobjective genetic algorithm Pareto optimality

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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 multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference

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PATTERN RECOGNITION 2005年第9期38卷 1444-1456页

作者： Delgado, M Pegalajar, MC Univ Granada Dept Ciencias Computac & Intelegencia Artificial ETSi Informat E-18071 Granada Spain

Grammatical inference has been extensively studied in recent years as a result of its wide field of application, and in turn, recurrent neural networks have proved themselves to be a good tool for grammatical inference. The learning algorithms for these neural networks, however, have been far less studied than those for feed-forward neural networks. Classical training methods for recurrent neural networks suffer from being trapped in local minimal and having a high computational time. In addition, selecting the optimal size of a neural network for a particular application is a difficult task. This suggests that the problems of developing methods to determine optimal topologies and new training algorithms should be studied. In this paper, we present a multi-objective evolutionary algorithm which is able to determine the optimal size of recurrent neural networks in any particular application. This is specially analyzed in the case of grammatical inference: in particular, we study how to establish the optimal size of a recurrent neural network in order to learn positive and negative examples in a certain language, and how to determine the corresponding automaton using a self-organizing map once the training has been completed. (c) 2005 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

关键词： recurrent neural networks multiobjective genetic algorithm grammatical inference

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Optimization of short-haul aircraft schedule recovery problems using a hybrid multiobjective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 2307-2315页

作者： Liu, Tung-Kuan Chen, Chiu-Hung Chou, Jyh-Horng Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan

A hybrid multiobjective genetic algorithm is presented in this paper to find an efficient solution for the daily short-haul aircraft schedule recovery problems which usually happen due to some disturbance events and require a time-sensitive solution to meet various hard constraints and soft objectives. The proposed algorithm employs an adaptive evaluated vector (AEV) to guide the solution search and uses the method of inequality-based multiobjective genetic algorithm to provide the multiobjective solution. A simulated disturbance experiment, temporal airport closure, is made and shown that the hybrid method can provide a very efficient short-haul schedule recovery solution under various performance indices. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multiobjective genetic algorithm Aircraft schedule recovery Short-haul Airline Combinational optimization

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Real-time task scheduling by multiobjective genetic algorithm

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JOURNAL OF SYSTEMS AND SOFTWARE 2009年第4期82卷 619-628页

作者： Yoo, Myungryun Musashi Inst Technol Fac Knowledge Engn Setagaya Ku Tokyo 1588557 Japan

Real-time tasks are characterized by computational activities with timing constraints and classified into two categories: a hard real-time task and a soft real-time task. In hard real-time tasks, tardiness can be catastrophic. The goal of hard real-time tasks scheduling algorithms is to meet all tasks' deadlines, in other words, to keep the feasibility of scheduling through admission control. However, in the case of soft real-time tasks, slight violation of deadlines is not so critical. In this paper, we propose a new scheduling algorithm for soft real-time tasks using multiobjective genetic algorithm (moGA) on multiprocessors system. It is assumed that tasks have precedence relations among them and are executed on homogeneous multiprocessor environment. The objective of the proposed scheduling algorithm is to minimize the total tardiness and total number of processors used. For these objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Soft real-time task multiobjective genetic algorithm

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Lifetime maximisation of disjoint wireless sensor networks using multiobjective genetic algorithm

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IET WIRELESS SENSOR SYSTEMS 2018年第5期8卷 200-207页

作者： El-Sherif, Mohamed Fahmy, Yasmine Kamal, Hanan Cairo Univ Fac Engn Elect & Commun Dept Giza Egypt

One of the lifetime maximisation methods for wireless sensor network (WSN) depends on organising the dense sensors into groups which can work in a cooperative sequential manner. Each group contains a subset of sensors that cover all the monitored area and is called a complete cover or simply a cover. Increasing the number of organised covers and maximising the covers lifetime enable longer network lifetime. Here, the authors investigate the WSN lifetime problem as a two-objective optimisation problem. The first objective is to find the maximum number of covers. The second objective considers the problem of wasted energy. Minimising the wasted energy in the critical sensors is achieved by defining a difference factor (DF). The DF is an indication of the difference between the critical sensor lifetime and the cover lifetime. This second objective is compared with other choices in the literature such as minimising the overlapping and minimising the variance. This optimisation problem is addressed using non-dominated sorting genetic algorithm-II (NSGA-II). Simulation results are conducted for the network lifetime when using one-objective and different two-objective optimisation problem. The choice of DF as the second objective is proved to overcome drawbacks of other second objectives choices.

关键词： wireless sensor networks genetic algorithms telecommunication network reliability energy measurement minimisation DF two-objective optimisation problem multiobjective genetic algorithm lifetime maximisation methods wireless sensor network WSN lifetime problem nondominated sorting genetic algorithm-II difference factor NSGA-II

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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

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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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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