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Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1843-1854页

作者： Li, Zhiyong Rudolph, Guenter Li, Kenli Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Univ Dortmund Lehrstuhl Algorithm Engn D-44221 Dortmund Germany

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H-epsilon gate and R&N-epsilon gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H-epsilon gate and R&N-epsilon gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N-epsilon gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate E value regions for two Q-gates are verified. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithms multi-objective 0/1 knapsack problems Quantum computing Convergence performance

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Simultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithms

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ENGINEERING WITH COMPUTERS 2019年第2期35卷 721-740页

作者： Techasen, Teerapol Wansasueb, Kittinan Panagant, Natee Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Sustainable & Infrastruct Res & Dev Ctr Fac Engn Khon Kaen 40002 Thailand

This paper proposes the design of trusses using simultaneous topology, shape, and size design variables and reliability optimization. objective functions consist of structural mass and reliability, while the probability of failure is set as a design constraint. Design variables are treated to simultaneously determine structural topology, shape, and sizes. Six test problems are posed and solved by a number of multi-objective evolutionary algorithms, and it is found that Hybridized Real-Code Population-Based Incremental Learning and Differential Evolution is the best performer. This work is considered an initial study for the combination of reliability optimization and simultaneous topology, shape, and sizing optimization of trusses.

关键词： Truss optimization multi-objective evolutionary algorithms Pareto dominance Reliability index

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A Co-evolutionary Scheme for multi-objective evolutionary algorithms Based on ε-Dominance

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IEEE ACCESS 2019年 7卷 18267-18283页

作者： Menchaca-Mendez, Adriana Montero, Elizabeth Miguel Antonio, Luis Zapotecas-Martinez, Saul Coello Coello, Carlos A. Riff, Maria-Cristina Univ Nacl Autonoma Mexico ENES Tecnol Informac Ciencias Campus Morelia Morelia 58190 Michoacan Mexico Univ Andres Bello Fac Ingn Vina Del Mar 2531015 Chile Inst Politecn Nacl Ctr Invest & Estudios Avanzados Dept Computac Ciudad De Mexico 07360 Mexico Univ Autonoma Metropolitana Unidad Cuajimalpa Dept Matemat Aplicadas & Sistemas Ciudad De Mexico 05348 Mexico Univ Tecn Federico Santa Maria Dept Informat Valparaiso 2390123 Chile

Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the epsilon-dominance has shown a proper balance between convergence and diversity. When using epsilon-dominance, diversity is ensured by partitioning the objective space into boxes of size epsilon and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of epsilon. In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of epsilon along the search without any need of a previous user's knowledge. We include the proposed co-evolutionary scheme into an MOEA based on epsilon-dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning stateof-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient.

关键词： multi-objective evolutionary algorithms epsilon-dominance co-evolutionary schemes parameter setting

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Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第8期26卷 1781-1794页

作者： Zavoianu, Alexandru-Ciprian Bramerdorfer, Gerd Lughofer, Edwin Silber, Siegfried Amrhein, Wolfgang Klement, Erich Peter Johannes Kepler Univ Linz Fuzzy Log Lab Linz Hagenberg Dept Knowledge Based Math Syst Linz Austria Johannes Kepler Univ Linz Inst Elect Drives & Power Elect Linz Austria Austrian Ctr Competence Mechatron ACCM Linz Austria

Performance optimization of electrical drives implies a lot of degrees of freedom in the variation of design parameters, which in turn makes the process overly complex and sometimes impossible to handle for classical analytical optimization approaches. This, and the fact that multiple non-independent design parameter have to be optimized synchronously, makes a soft computing approach based on multi-objective evolutionary algorithms (MOEAs) a feasible alternative. In this paper, we describe the application of the well known Non-dominated Sorting Genetic Algorithm II (NSGA-II) in order to obtain high-quality Pareto-optimal solutions for three optimization scenarios. The nature of these scenarios requires the usage of fitness evaluation functions that rely on very time-intensive finite element (FE) simulations. The key and novel aspect of our optimization procedure is the on-the-fly automated creation of highly accurate and stable surrogate fitness functions based on artificial neural networks (ANNs). We employ these surrogate fitness functions in the middle and end parts of the NSGA-II run (-> hybridization) in order to significantly reduce the very high computational effort required by the optimization process. The results show that by using this hybrid optimization procedure, the computation time of a single optimization run can be reduced by 46-72% while achieving Pareto-optimal solution sets with similar, or even slightly better, quality as those obtained when conducting NSGA-II runs that use FE simulations over the whole run-time of the optimization process. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Electrical drives Performance optimization multi-objective evolutionary algorithms Feed-forward artificial neural networks Surrogate fitness evaluation Hybridization

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An alternative hypervolume-based selection mechanism for multi-objective evolutionary algorithms

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SOFT COMPUTING 2017年第4期21卷 861-884页

作者： Menchaca-Mendez, Adriana Coello Coello, Carlos A. CINVESTAV IPN Evolutionary Computat Grp Dept Computac Ave IPN 2508 San Pedro Zacatenco Mexico City 07300 DF Mexico

In this paper, we are interested in selection mechanisms based on the hypervolume indicator with a particular emphasis on the mechanism used in an improved version of the S metric selection evolutionary multi-objective algorithm (SMS-EMOA) called iSMS-EMOA, which exploits the locality property of the hypervolume. Here, we propose a new selection scheme which approximates the contribution of solutions to the hypervolume and it is designed to preserve the locality property exploited by iSMS-EMOA. This approach is proposed as an alternative to the use of exact hypervolume calculations and is aimed for solving many-objective optimization problems. The proposed approach is called "approximate version of the improved SMS-EMOA (aviSMS-EMOA)" and is validated using standard test problems (with three or more objectives) and performance indicators taken from the specialized literature. Our preliminary results indicate that our proposed approach is a good alternative to solve many-objective optimization problems, if we consider both quality in the solutions and running time required to obtain them because it outperforms two versions of the original SMS-EMOA that approximate the contributions to the hypervolume, it outperforms MOEA/D using penalty boundary intersection and it is competitive with respect to the original SMS-EMOA in several of the test problems adopted. Also, its computational cost is reasonable (it is slower than MOEA/D, but faster than SMS-EMOA).

关键词： multi-objective evolutionary algorithms Selection operators Hypervolume indicator

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Natural laminar flow airfoil shape design at transonic regimes with multi-objective evolutionary algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第3期233卷 991-999页

作者： Chen, Yongbin Tang, Zhili Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

The natural laminar flow airfoil shape design at transonic regime is solved using multi-objective evolutionary algorithms in this paper. A shock wave control bump is used to reduce wave drag of natural laminar flow airfoil in transonic flow, and the e(N) transition prediction method based on the linear stability theory is used to predict the transition location. The multi-island parallel multi-objective evolutionary algorithms is implemented to optimize the airfoil shape equipped with shock wave control bump for obtaining a larger laminar flow region and a weaker wave drag simultaneously. Optimization experiment shows that it is easy to capture the Pareto front of wave drag minimization and laminar flow region maximization. Results demonstrate that both wave drag and friction drag performance of several chosen Pareto members are significantly improved via the optimal airfoil shape and shock wave control bump device compared to that of the baseline shape.

关键词： Natural laminar flow airfoil transition prediction linear stability theory active shock control bump multi-objective evolutionary algorithms

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EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms

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NEURAL COMPUTING & APPLICATIONS 2024年第13期36卷 7221-7243页

作者： Guarino, Alfonso Santoro, Domenico Grilli, Luca Zaccagnino, Rocco Balbi, Mario Univ Salerno Dept Comp Sci Via Giovanni Paolo 2130 Fisciano SA Italy Univ Bari Dept Econ & Finance Largo Abbazia S Scolast I-70124 Bari BA Italy Univ Foggia Dept Econ Management & Terr Via Zara 11 I-71121 Foggia FG Italy

Optimal portfolio selection-composing a set of stocks/assets that provide high yields/returns with a reasonable risk-has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users' preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor's preferences has proved to be very effective in the portfolio's composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation.

关键词： Portfolio optimization multi-objective evolutionary algorithms Stock market Realistic genetic operator

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Preventive maintenance optimisation of multi-equipment manufacturing systems by combining discrete event simulation and multi-objective evolutionary algorithms

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PRODUCTION PLANNING & CONTROL 2008年第4期19卷 342-355页

作者： Oyarbide-Zubillaga, A. Goti, A. Sanchez, A. Univ Mondragon Ind Management Dept Arrasate Mondragon Spain Univ Politecn Valencia Dept Stat & Operat Res E-46071 Valencia Spain

This paper is focused on preventive maintenance optimisation in manufacturing environments, with the objective of determining the optimal preventive maintenance frequencies for multi-equipment systems under cost and profit criteria. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multi-objective evolutionary algorithms have shown to deal with multi-objective problems to develop a maintenance management and optimisation approach. An application case is performed where the activities applied on a system that produces hubcaps for the car making industry, showing the quantitative benefits of adopting the detailed approach.

关键词： preventive maintenance multi-equipment optimisation discrete event simulation multi-objective evolutionary algorithms metaheuristics

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Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulation

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SOFT COMPUTING 2023年第24期27卷 19213-19246页

作者： Cacereno, Andres Greiner, David Galvan, Blas Univ Las Palmas de Gran Canaria ULPGC Inst Univ Sistemas Inteligentes & Aplicac Numer In Campus Univ Tafira Las Palmas Gran Canaria 35017 Las Palmas Spain

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling evolutionary algorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance-consisting of achieving the optimum period of time to carry out a preventive maintenance activity-are taken into account before being considered by the authors of the present paper. This work couples multi-objective evolutionary algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the multi-objective evolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed multi-objective evolutionary algorithms is handled;moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of multi-objective evolutionary algorithms and Discrete Event Simulation. Finally, the methodology is

关键词： multi-objective evolutionary algorithms Availability Optimum design Preventive maintenance

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Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms

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ARTIFICIAL INTELLIGENCE 2019年 275卷 279-294页

作者： Qian, Chao Yu, Yang Tang, Ke Yao, Xin Zhou, Zhi-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimization problems. During the past two decades, promising results on the running time analysis (one essential theoretical aspect) of EAs have been obtained, while most of them focused on isolated combinatorial optimization problems, which do not reflect the general-purpose nature of EAs. To provide a general theoretical explanation of the behavior of EAs, it is desirable to study their performance on general classes of combinatorial optimization problems. To the best of our knowledge, the only result towards this direction is the provably good approximation guarantees of EAs for the problem class of maximizing monotone submodular functions with matroid constraints. The aim of this work is to contribute to this line of research. Considering that many combinatorial optimization problems involve non-monotone or non-submodular objective functions, we study the general problem classes, maximizing submodular functions with/without a size constraint and maximizing monotone approximately submodular functions with a size constraint. We prove that a simple multi-objective EA called GSEMO-C can generally achieve good approximation guarantees in polynomial expected running time. (C) 2019 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms Submodular optimization multi-objective evolutionary algorithms Running time analysis Computational complexity

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