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

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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Performance, Energy, and Temperature Enabled Task Scheduling using evolutionary Techniques

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2019年 22卷 272-286页

作者： Sheikh, Hafiz Fahad Ahmad, Ishfaq Arshad, Sheheryar Ali Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front. (C) 2017 Published by Elsevier Inc.

关键词： Task scheduling Dynamic thermal management multi-objective evolutionary algorithms multi-core systems DAG scheduling

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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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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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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 based on Pareto prediction for automatic test task scheduling problems

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APPLIED SOFT COMPUTING 2018年 66卷 394-412页

作者： Lu, Hui Zhou, Rongrong Fei, Zongming Shi, Jinhua Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Kentucky Dept Comp Sci Lexington KY 40506 USA

The test task scheduling problem (TTSP) is a combinatorial optimization problem still under investigation. A multi-objective evolutionary algorithm based on Pareto prediction (PP-MOEA) is proposed fully considering the characteristics of TTSP. In a multi-objective TTSP, multiple solutions in the decision space correspond to a point in the objective space and the number of true Pareto front in the objective space is relatively small. Moreover, there are less solutions distributing in either high or low objective values while most solutions distribute in average. Based on these characteristics, a pure elitism strategy and a novel scale-down non-dominated sorting (SDNS) method are combined to improve the efficiency and convergence of the algorithm. Additionally, an extended encoding range approach and a Pareto prediction strategy are proposed to help exploring new solutions and increase the diversity of population. The Pareto prediction strategy is implemented by following the historical evolutionary information of the current Pareto set and then predicting the next generation of several individuals. The makespan (maximal test completion time) and the mean workload of the instruments are considered in this research. Based on the general framework of MOEAs, the proposed algorithm is more effective and efficient in tackling multi-objective TTSPs. PP-MOEA has the better performance of convergence and diversity compared with other algorithms based on the statistical analysis of experiments results. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithms Scheduling theory Encoding mechanism Pareto prediction strategy Scale-down non-dominated sorting method

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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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