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IEEE Congress on Evolutionary Computation (CEC)

作者： Lohpetch, Dome Corne, David Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781424478354

Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work in this area used GP to find rules that were profitable, but were nevertheless outperformed by the simple "buy and hold" (B&H) strategy. Attempts since then tend to report similar findings, except for a handful of cases where GP methods have been found to outperform B&H. Recent work has clarified that robust outperformance of B&H depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that amount to sound engineering of the GP grammar and the validation strategy. Here we add a comprehensive study of multiobjective approaches to this investigation, and find that multiobjective strategies provide even more robustness in outperforming B&H, even in the context of more frequent (e.g. weekly) trading decisions.

关键词： financial trading genetic programming multiobjective algorithms

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A Comparison of multiobjective algorithms in Evolving Quadrupedal Gaits 1

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14th International Conference on the Simulation of Adaptive Behavior (SAB)

作者： Moore, Jared M. McKinley, Philip K. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (数字)9783319434889

ISBN: (纸本)9783319434889;9783319434872

Robotic systems, whether physical or virtual, must balance multiple objectives to operate effectively. Beyond performance metrics such as speed and turning radius, efficiency of movement, stability, and other objectives contribute to the overall functionality of a system. Optimizing multiple objectives requires algorithms that explore and balance improvements in each. In this paper, we evaluate and compare two multiobjective algorithms, NSGA-II and the recently proposed Lexicase selection, investigating distance traveled, efficiency, and vertical torso movement for evolving gaits in quadrupedal animats. We explore several variations of Lexicase selection, including different parameter configurations and weighting strategies. A control treatment evolving solely on distance traveled is also presented as a baseline. All three algorithms (NSGA-II, Lexicase, and Control) produce effective locomotion in the quadrupedal animat, but differences arise in performance and efficiency of movement. The NSGA-II algorithm significantly outperforms Lexicase selection in all three objectives, while Lexicase selection significantly outperforms the control in two of the three objectives.

关键词： Evolutionary robotics multiobjective algorithms Genetic algorithms Computational evolution Lexicase selection NSGA-II

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A hybrid novel SVM model for predicting CO2 emissions using multiobjective Seagull Optimization

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第46期28卷 66171-66192页

作者： Ehteram, Mohammad Sammen, Saad Sh. Panahi, Fatemeh Sidek, Lariyah Mohd Semnan Univ Fac Civil Engn 4 Dept Water Engn & Hydraul Struct Semnan Iran Univ Diyala Coll Engn Dept Civil Engn Baqubah Diyala Governor Iraq Univ Kashan Fac Nat Resources & Earth Sci Kashan Iran Univ Tenaga Nas Inst Energy Infrastruct Jalan IKRAM UNITEN Kajang 43000 Selangor Malaysia

The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP(2)), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP(2)), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.

关键词： CO2 emissions SVM multiobjective algorithms Environment management

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Multi-objective optimisation-based active distribution system planning with reconfiguration, intermittent RES, and DSTATCOM

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IET RENEWABLE POWER GENERATION 2019年第13期13卷 2418-2429页

作者： Sannigrahi, Surajit Ghatak, Sriparna Roy Acharjee, Parimal NIT Durgapur Elect Engn Dept MG Ave Durgapur 713209 W Bengal India KIIT Univ Sch Elect Engn Bhubaneswar 751024 Odisha India

The enormous load growth in recent times has forced distribution companies to undertake comprehensive planning of the active distribution system (ADS) to maintain superior service to their consumers. Under different critical situations in the restructured power system, reconfiguration in combination with the incorporation of renewable energy sources (RESs) and distributed static compensator (DSTATCOM) must be utilised for accurate system planning. In addition, from a practical viewpoint, the time-variant load demand of different consumers and the intermittency of RES units must be considered. This study proposes a modified multi-objective particle swarm optimisation (m-MOPSO) technique for ADS planning considering reconfiguration, RES, and DSTATCOM to enhance voltage stability, reduce pollution, improve reliability, and maximise financial benefits. In the proposed m-MOPSO, a novel non-dominant sorting strategy is used to maintain diversity among the non-dominated solutions. The time-varying system load, yearly load growth, and intermittent power generation of RES are considered to construct a realistic planning model. The proposed technique is tested on the 33-bus ADS considering different planning schemes to provide the most suitable planning scheme to the ADS planners. Moreover, the accuracy of the proposed algorithm is confirmed by comparing it with other multi-objective algorithms.

关键词： genetic algorithms static VAr compensators distributed power generation power distribution economics renewable energy sources Pareto optimisation particle swarm optimisation power distribution planning multiobjective optimisation-based active distribution system planning reconfiguration intermittent RES DSTATCOM enormous load growth recent times distribution companies comprehensive planning different critical situations restructured power system renewable energy sources accurate system planning time-variant load demand different consumers RES units multiobjective particle swarm optimisation technique novel nondominant sorting strategy time-varying system load yearly load growth intermittent power generation realistic planning model 33-bus ADS different planning schemes suitable planning scheme multiobjective algorithms

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

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NEURAL COMPUTING & APPLICATIONS 2022年第8期34卷 6307-6329页

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Charmo Univ Coll Med & Appl Sci Appl Comp Dept Sulaimany Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Informat Technol Sulaimany Iraq Univ Human Dev Coll Sci & Technol Dept Informat Technol Sulaimany Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-Objective Dragonfly Algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms.

关键词： multiobjective algorithms multiobjective evolutionary algorithms MOLPB LPB Learner performance-based behavior algorithm Optimization Metaheuristic optimization algorithm

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Fast and optimised design of a differential VCO using symbolic technique and multi objective algorithms

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IET CIRCUITS DEVICES & SYSTEMS 2019年第8期13卷 1187-1195页

作者： Panda, Madhusmita Patnaik, Santosh Kumar Mal, Ashis Kumar Ghosh, Sumalya SOA Deemed Be Univ Dept Elect & Commun Engn Bhubaneswar Odisha India NIST Dept Elect & Commun Engn Berhampur Odisha India NIT Dept Elect & Commun Engn Durgapur India

In this work, a DVCO has been designed for a 4-bit, 10 MHz VCO based ADC. The noise modelling and analysis of this designed DVCO is carried out using layered determinant expansion based DDD technique. The results obtained using these methods are found to be nearly identical to that of SPICE. However, the computational time has been reduced from 13.7 sec using numerical method (SPICE) to 4.5 sec using DDD technique. Optimisation of the designed DVCO is then carried out using multi-objective optimisation techniques such as IDEA and MOPSO to enhance the performance. Low power and low phase noise at the desired frequency of oscillation were the optimisation goals. For this designed DVCO, IDEA optimisation approach seems to be more efficient than the MOPSO. The optimised DVCO is then simulated at different process corners using SPICE. The designed DVCO has shown improvement in phase noise from -80.3 dBc/Hz to -88.9 dBc/Hz at 1 MHz offset. The power consumption is also reduced from 38.4 mw to 34.5 mw and achieved a target frequency of 3.49 GHz. These improvements in the performance of the DVCO lead to an improvement in the ENOB from 3.6 to 4.2 bit of the designed ADC.

关键词： evolutionary computation particle swarm optimisation analogue-digital conversion voltage-controlled oscillators phase noise low-power electronics microwave oscillators decision diagrams integrated circuit noise word length 4 0 bit frequency 3 49 GHz frequency 1 0 MHz frequency 10 0 MHz power consumption nfeasibility-driven evolutionary algorithm noise analysis noise modelling differential voltage controlled oscillator layered determinant expansion VCO-based ADC optimum design parameters low-phase noise multiobjective particle swarm optimisation DDD technique symbolic method multiobjective algorithms differential VCO SPICE IDEA optimisation approach

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Parallel evolutionary computation for multiobjective gene interaction analysis

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JOURNAL OF COMPUTATIONAL SCIENCE 2020年 40卷

作者： Goncalves, Francisco Santander-Jimenez, Sergio Sousa, Leonel Granado-Criado, Jose M. Ilic, Aleksandar Univ Lisbon Inst Engn Sistemas & Comp Invest & Desenvolviment Inst Super Tecn P-1000029 Lisbon Portugal Univ Extremadura Inst Invest Tecnol Informat Aplicadas Extremadura Avda Univ S-N Caceres 10003 Spain

Multiple studies provide evidence on the impact of certain gene interactions in the occurrence of diseases. Due to the complexity of genotype-phenotype relationships, it is required the development of highly efficient algorithmic strategies that successfully identify high-order interactions attending to different evaluation criteria. This work investigates parallel evolutionary computation approaches for multiobjective gene interaction analysis. A multiobjective genetic algorithm, with novel optimized design features, is developed and parallelized under problem-independent and problem-dependent schemes. Experimental results show the relevant performance of the method for complex interaction orders, significantly accelerating execution time (up to 296x) with regard to other state-of-the-art multiobjective tools. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Parallelism Evolutionary computation multiobjective algorithms Bioinformatics Biomedicine

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multiobjective training of artificial neural networks for rainfall-runoff modeling

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WATER RESOURCES RESEARCH 2008年第8期44卷

作者： de Vos, N. J. Rientjes, T. H. M. Delft Univ Technol Water Resources Sect NL-2600 AA Delft Netherlands Inst Geoinformat Sci & Earth Observat Dept Water Resources NL-7500 AA Enschede Netherlands

This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for this purpose. The performances of the multiobjective algorithms Multi Objective Shuffled Complex Evolution Metropolis-University of Arizona (MOSCEM-UA) and Nondominated Sorting Genetic Algorithm II (NSGA-II) have been compared to the single-objective Levenberg-Marquardt and Genetic Algorithm for training of these models. Performance has been evaluated by means of a number of commonly applied objective functions and also by investigating the internal weights of the networks. Additionally, the effectiveness of a new objective function called mean squared derivative error, which penalizes models for timing errors and noisy signals, has been explored. The results show that the multiobjective algorithms give competitive results compared to the single-objective ones. Performance measures and posterior weight distributions of the various algorithms suggest that multiobjective algorithms are more consistent in finding good optima than are single-objective algorithms. However, results also show that it is difficult to conclude if any of the algorithms is superior in terms of accuracy, consistency, and reliability. Besides the training algorithm, network performance is also shown to be sensitive to the choice of objective function(s), and including more than one objective function proves to be helpful in constraining the neural network training.

关键词： runoff forecasting artificial neural networks multiobjective algorithms model calibration

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An hybrid evolutionary multiobjective algorithm for multiuser margin maximization in DSL

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2016年第1期29卷 194-209页

作者： Gomes, Ana Monteiro, Marcio Dortschy, Boris Klautau, Aldebaro Fed Univ Para Signal Proc Lab Rua Augusto Correa01-Guama BR-66075110 Belem Para Brazil Lund Univ Dept Elect & Informat Technol Lund Sweden Ericsson Res Broadband Technol Stockholm Sweden

This work describes and proposes the application of evolutionary algorithms on the multiuser spectrum and SNR margin optimization problem for multicarrier systems, such as digital subscriber line. The proposed method is designed such that it takes advantage of special characteristics of the well-known power adaptation techniques and uses them to solve the broader and more challenging problem of multiuser margin adaptation. Simulations show that the proposed method provides Pareto-optimal and diverse solutions when compared to a previous method to solve the same problem. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： digital subscriber line (DSL) multiobjective algorithms multiuser margin optimization

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Multilevel multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation

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JOURNAL OF IMAGING 2023年第4期9卷 78-78页

作者： Kanadath, Anusree Jothi, J. Angel Arul Urolagin, Siddhaling Birla Inst Technol & Sci Pilani Dept Comp Sci Dubai Int Acad City POB 345055 Dubai U Arab Emirates

Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm intelligence for segmenting histopathology images is less explored. In this study, we introduce a Multilevel multiobjective Particle Swarm Optimization guided Superpixel algorithm (MMPSO-S) for the effective detection and segmentation of various regions of interest (ROIs) from Hematoxylin and Eosin (H&E)-stained histopathology images. Several experiments are conducted on four different datasets such as TNBC, MoNuSeg, MoNuSAC, and LD to ascertain the performance of the proposed algorithm. For the TNBC dataset, the algorithm achieves a Jaccard coefficient of 0.49, a Dice coefficient of 0.65, and an F-measure of 0.65. For the MoNuSeg dataset, the algorithm achieves a Jaccard coefficient of 0.56, a Dice coefficient of 0.72, and an F-measure of 0.72. Finally, for the LD dataset, the algorithm achieves a precision of 0.96, a recall of 0.99, and an F-measure of 0.98. The comparative results demonstrate the superiority of the proposed method over the simple Particle Swarm Optimization (PSO) algorithm, its variants (Darwinian particle swarm optimization (DPSO), fractional order Darwinian particle swarm optimization (FODPSO)), multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), non-dominated sorting genetic algorithm 2 (NSGA2), and other state-of-the-art traditional image processing methods.

关键词： nature-inspired algorithms particle swarm optimization multiobjective algorithms image segmentation thresholding histopathology

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