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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 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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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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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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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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A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2020年第1期28卷 21-46页

作者： Lopes Alexandrino, Patricia da Silva Gomes, Guilherme Ferreira Cunha, Sebastiao Simoes, Jr. Univ Fed Itajuba Mech Engn Inst Itajuba MG Brazil

An inverse problem of damage identification and localization in a structure is modelled as a robust optimization problem. In the robust optimization problem, the optimum value and small variations around this optimum value are considered. The structural health monitoring damage detection problem is solved using a multiobjective genetic algorithm. So, the robust optimum value is obtained by solving a multiobjective problem where a functional and a variance function of this functional are used. This variance function is obtained by a Design of Experiment with regression and also through a relation between functional variance and damage parameters found by artificial neural network. As a multiobjective genetic algorithm obtains multiple solutions, a fuzzy decision making technique finds the better tradeoff solution for the problem. Boundary element method is utilized to obtain the distribution of stress to elastostatic problem. Numerical results clearly show that the proposed strategy and the use an optimized fuzzy decision making results in accurate damage identification and represents a powerful tool for structural health monitoring. Based on the analysis and numerical results, suggestions to potential researchers have also been provided for future scopes.

关键词： Damage detection fuzzy decision making multiobjective genetic algorithm artificial neural network boundary element method

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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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