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A new methodology for calculating roadway lighting design based on a multi-objective evolutionary algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2013年第6期40卷 2156-2164页

作者： Gomez-Lorente, Daniel Rabaza, O. Espin Estrella, A. Pena-Garcia, A. Univ Granada Dept Civil Engn E-18071 Granada Spain

This paper presents a new method for calculating the design of roadway lighting. Apart from its accuracy, this method, which is based on a multi-objective evolutionary algorithm, has the added advantage of enhancing the energy efficiency of lighting installations. This is positive because the economic use of energy resources is evidently a priority in the world today. In our study, an exhaustive calibration process was used to fine-tune the accuracy and precision of the new method presented. The results obtained were then compared with those of DIALUX, a well-known free software program that is frequently used for the design of lighting installations. In the second phase of this research, the lighting installation was made more complex in order to verify the applicability of this new method to a wide range of different contexts. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms Numerical optimization Roadway lighting Installation efficiency

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MRMOGA: a new parallel multi-objective evolutionary algorithm based on the use of multiple resolutions

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2007年第4期19卷 397-441页

作者： Lopez Jaimes, Antonio Coello Coello, Carlos A. CINVESTAV IPN Evolutionary Computat Grp EVOCINVDept Intn ElectSecc Computac Mexico City 07360 DF Mexico

In this paper, we introduce MRMOGA (multiple Resolution multi-objective Genetic Algorithm), a new parallel multi-objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well-defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state-of-the-art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi-objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright (c) 2006 John Wiley & Soris, Ltd.

关键词： parallel processing multi-objective evolutionary algorithms parallel multi-objective evolutionary algorithms multi-objective optimization evolutionary algorithms

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Proposal and Comparative Study of evolutionary algorithms for Optimum Design of a Gear System

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IEEE ACCESS 2020年 8卷 3482-3497页

作者： Mendez, Maximo Rossit, Daniel A. Gonzalez, Begona Frutos, Mariano Univ Las Palmas de Gran Canaria ULPGC Inst Univ SIANI Las Palmas Gran Canaria 35001 Spain Univ Nacl Sur Dept Engn RA-8000 Bahia Blanca Buenos Aires Argentina INMABB CONICET RA-8000 Bahia Blanca Buenos Aires Argentina IIESS CONICET RA-8002 Bahia Blanca Buenos Aires Argentina

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.

关键词： Differential evolution evolutionary computation gear train optimization genetic algorithms mechanical engineering multi-objective evolutionary algorithms non-dominated sorting genetic algorithm-II

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A methodology for evaluating multi-objective evolutionary feature selection for classification in the context of virtual screening

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SOFT COMPUTING 2019年第18期23卷 8775-8800页

作者： Jimenez, Fernando Perez-Sanchez, Horacio Palma, Jose Sanchez, Gracia Martinez, Carlos Univ Murcia Fac Informat Dept Informat & Commun Engn E-30100 Murcia Spain Catholic Univ San Antonio Murcia UCAM Comp Engn Dept Bioinformat & High Performance Comp Res Grp BIOHP Murcia 30107 Spain Univ Murcia Int Doctorate Sch E-30100 Murcia Spain

Virtual screening (VS) methods have been shown to increase success rates in many drug discovery campaigns, when they complement experimental approaches, such as high-throughput screening methods or classical medicinal chemistry approaches. Nevertheless, predictive capability of VS is not yet optimal, mainly due to limitations in the underlying physical principles describing drug binding phenomena. One approach that can improve VS methods is the aid of machine learning methods. When enough experimental data are available to train such methods, predictive capability can considerably increase. We show in this research work how a multi-objective evolutionary search strategy for feature selection, which can provide with small and accurate decision trees that can be very easily understood by chemists, can drastically increase the applicability and predictive ability of these techniques and therefore aid considerable in the drug discovery problem. With the proposed methodology, we find classification models with accuracy between 0.9934 and 1.00 and area under ROC between 0.96 and 1.00 evaluated in full training sets, and accuracy between 0.9849 and 0.9940 and area under ROC between 0.89 and 0.93 evaluated with tenfold cross-validation over 30 iterations, while substantially reducing the model size.

关键词： Feature selection multi-objective evolutionary algorithms Classification Decision trees Virtual screening Drug discovery

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An adaptive hybrid evolutionary algorithm and its application in aeroengine maintenance scheduling problem

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SOFT COMPUTING 2021年第8期25卷 6527-6538页

作者： Fu, Guo-Zhong Huang, Hong-Zhong Li, Yan-Feng Zhou, Jie Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Sichuan Peoples R China

multi-objective evolutionary algorithms (MOEAs) have been successfully employed to solve many scientific and engineering problems. However, many algorithms perform ill in maintaining diversity and convergence simultaneously. In this paper, we devised a novel operator selection framework based on two collaborative indicators, generational distance (GD) and maximum spread (MS) to improve the diversity while maintaining a good convergence. By calculating the variation of GDs and MSs over the past 7 iterations, an instruction is conveyed to select a proper operator to execute next 7 iterations. This process is repeated until it reaches the maximum iteration. Two operators are embedded in this algorithm which are differential evolution operator (DE/rand/1) and our proposed crow search operator which is deemed to be efficient in explorating the search space. MOEA/D is utilized as the basis framework of our proposed algorithm. Experiments indicate that our proposed algorithm is valid and outperforms other famous algorithms in terms of diversity and convergence. In the end, a particular aeroengine maintenance scheduling problem is solved by our proposed algorithm.

关键词： multi-objective evolutionary algorithms Collaborative indicator-based operator selection Differential evolution Crow search Maintenance scheduling problem

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Handling performance optimisation for formula vehicle using multi-objectives evolutionary algorithms

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VEHICLE SYSTEM DYNAMICS 2020年第12期58卷 1823-1838页

作者： Tey, J. Y. Rahizar, R. Univ Tunku Abdul Rahman Dept Mech & Mat Engn Lee Kong Chian Fac Engn & Sci Selangor Malaysia Univ Malaya Dept Mech Engn Fac Engn Adv Computat & Appl Mech ACAM Res Grp Kuala Lumpur Malaysia

A method based on constraint multi-objective evolutionary algorithms approach is proposed to optimise the handling performance of a race car dynamic. This involves a software-in-the-loop optimisation (SiLO) between a high-fidelity model of a formula vehicle modelled in VI-Grade and MATLAB for hosting the optimisation algorithms. The optimisation process involves the tuning of the suspension design variables, i.e. spring stiffness, anti-roll bar stiffness, damper coefficient, toe angle, and camber angle for both front and rear suspensions against eight objectives functions, i.e. sideslip overshoot, yaw rate overshoot, linear understeer gradient, limit understeer gradient, maximum lateral acceleration, yaw rate phase delay and lateral acceleration phase delay. A custom constraint function was introduced to improve the convergence speed of the optimisation process. The optimised result showed a promising increase in handling performances, i.e. the maximum achievable lateral acceleration has increased by 8.9% whilst maintaining the stability of the vehicle close to neutral steer behaviour. The sideslip angle was well controlled under 1 deg at 95% of maximum lateral acceleration, and slip angle overshoot has improved by 1.65% in step steer manoeuvre. Additionally, the skid-pad events and autocross event laps time were reduced by 5.19% and 1.71%, respectively.

关键词： Handling performance multi-objective evolutionary algorithms NSGA-II CMOEA D-DE

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A comparative study of many-objective evolutionary algorithms for the discovery of software architectures

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EMPIRICAL SOFTWARE ENGINEERING 2016年第6期21卷 2546-2600页

作者： Ramirez, Aurora Raul Romero, Jose Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain

During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure such complex systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Along with the expected functionality, non-functional requirements are key at this stage to guide the many design alternatives to be evaluated by software architects. The appearance of Search Based Software Engineering (SBSE) brings an approach that supports the software engineer along the design process. evolutionary algorithms can be applied to deal with the abstract and highly combinatorial optimisation problem of architecture discovery from a multiple objective perspective. The definition and resolution of many-objective optimisation problems is currently becoming an emerging challenge in SBSE, where the application of sophisticated techniques within the evolutionary computation field needs to be considered. In this paper, diverse non-functional requirements are selected to guide the evolutionary search, leading to the definition of several optimisation problems with up to 9 metrics concerning the architectural maintainability. An empirical study of the behaviour of 8 multi- and many-objective evolutionary algorithms is presented, where the quality and type of the returned solutions are analysed and discussed from the perspective of both the evolutionary performance and those aspects of interest to the expert. Results show how some many-objective evolutionary algorithms provide useful mechanisms to effectively explore design alternatives on highly dimensional objective spaces.

关键词： Software architecture discovery Search based software engineering Many-objective evolutionary algorithms multi-objective evolutionary algorithms

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Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

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INFORMATION SCIENCES 2010年第10期180卷 1924-1941页

作者： Marcelloni, Francesco Vecchio, Massimo Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy INRIA Saclay Ile De France Sud France

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Wireless sensor networks Data compression multi-objective evolutionary algorithms Energy efficiency Signal processing

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A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving

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INFORMATION SCIENCES 2015年 321卷 14-30页

作者： Onieva, E. Hernandez-Jayo, U. Osaba, E. Perallos, A. Zhang, X. Univ Deusto Deusto Inst Technol Bilbao 48007 Spain City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

This paper focuses on the application of multi-objective evolutionary algorithms (MOEAs) to develop a Fuzzy Rule-Based System (FRBS) dedicated to manage the speed of an autonomous vehicle in an intersection scenario. Compared to other intersection scenarios, the main point here is that the autonomous vehicle is approaching an intersection that is being crossed by a row of manual vehicles those are not paying any attention to the presence of the autonomous vehicle, thus making coordination impossible. In this case, the autonomous vehicle bears sole responsibility for adapting its speed to the state of the other vehicles, with the aim of completing the maneuver safely and efficiently. The specific conditions of this problem make it complex because of the large time requirements needed to consider multiple criteria (which enlarge the solution search space) and the long computation time required in each evaluation. In addition, the large number of variables involved increases the complexity of the scenario. In this paper, a MOEA is proposed to obtain a more compact and efficient FRBS. The proposal is based on the well-known Strength Pareto evolutionary Algorithm 2 (SPEA2) technique, but uses different mechanisms for guiding the search towards the desired Pareto zone. The MOEA uses specific operators to deal with the problem, to inherit fitness values from one generation to the next, thus arranging that it is only necessary to execute one scenario per generation to obtain an FRBS that works fine in many situations. In addition, the most important rules are identified in each FRBS, with the aim of realizing balanced crossovers. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Intelligent transportation systems Autonomous driving Fuzzy logic control multi-objective evolutionary algorithms Fuzzy rule-based systems multi-objective evolutionary fuzzy systems

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multi-objective evolutionary optimization using the relationship between F1 and accuracy metrics in classification tasks

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APPLIED INTELLIGENCE 2019年第9期49卷 3447-3463页

作者： Carlos Fernandez, Juan Carbonero, Mariano Antonio Gutierrez, Pedro Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Univ Loyola Andalucia Dept Quantitat Methods Cordoba Spain

This work analyses the complementarity and contrast between two metrics commonly used for evaluating the quality of a binary classifier: the correct classification rate or accuracy, C, and the F-1 metric, which is very popular when dealing with imbalanced datasets. Based on this analysis, a set of constraints relating C and F-1 are defined as a function of the ratio of positive patterns in the dataset. We evaluate the possibility of using a multi-objective evolutionary algorithm guided by this pair of metrics to optimise binary classification models. To check the validity of the constraints, we perform an empirical analysis considering 26 benchmark datasets obtained from the UCI repository and an interesting liver transplant dataset. The results show that the relation is fulfilled and that the use of the algorithm for simultaneously optimising the pair (C,F-1) leads to a generally balanced accuracy for both classes. The experiments also reveal that, in some cases, better results are obtained by using the majority class as the positive class instead of using the minority one, which is the most common approach with imbalanced datasets.

关键词： Binary classification Evaluation metrics F-1-metric multi-objective evolutionary algorithms

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