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Cell formation using evolutionary algorithms with certain constraints

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2000年第1-3期64卷 267-278页

作者： Plaquin, MF Pierreval, H IFMA LIMOS Equipe Rech Syst Prod F-63175 Aubiere France

Cellular manufacturing is well known as an effective way to improve workshop performances. There are various methods to design cells. Most of them do not take into account constraints specific to the workshop. We are interested in the design of manufacturing cells that can take into account specific constraints (for example, certain machines may have to stay together in the same cell because they will share a common resource or certain machines may have to be separated because they will produce interferences). The proposed method uses evolutionary algorithms. The initial solutions are created with an algorithm based on a random tree search and then the solutions evolve thanks to operators designed so as to satisfy, at any stage, the constraints. The suggested method is illustrated through a problem with a known optimum in order to verify that the evolutionary algorithm can find the optimum solution. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： cellular manufacturing group technology evolutionary algorithms

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A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第5期37卷 3853-3862页

作者： Chan, K. Y. Kwong, C. K. Jiang, H. Aydin, M. E. Fogarty, T. C. Curtin Univ Technol Digital Ecosyst & Business Intelligence Inst Perth WA Australia Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Bedfordshire Dept Comp & Informat Syst Luton Beds England London S Bank Univ Fac Business Comp & Informat Management London England

Recent research shows that orthogonal array based crossovers outperform standard and existing crossovers in evolutionary algorithms in solving parametrical problems with high dimensions and multi-optima. However, those crossovers employed so far, ignore the consideration of interactions between genes. In this paper, we propose a method to improve the existing orthogonal array based crossovers by integrating information of interactions between genes. It is empirically shown that the proposed orthogonal array based crossover outperforms significantly both the existing orthogonal array based crossovers and standard crossovers on solving parametrical benchmark functions that interactions exist between variables. To further compare the proposed orthogonal array based crossover with the existing crossovers in evolutionary algorithms, a validation test based on car door design is used in which the effectiveness of the proposed orthogonal array based crossover is studied. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Crossover evolutionary algorithms Interactions between genes Orthogonal array Car door design

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Some techniques for analyzing time complexity of evolutionary algorithms

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2012年第6期34卷 755-766页

作者： Ding, Lixin Yu, Jinghu Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Wuhan Univ Technol Sch Sci Dept Math Wuhan 430070 Peoples R China

In this paper we introduce some techniques for the analysis of time complexity of evolutionary algorithms (EAs) based on a finite search space. The Markov property and the decomposition of a matrix are employed for the exact analytic expressions of the mean first hitting times that EAs reach the optimal solutions (FHT-OS). Dynkin's formula, one-step increment analysis and some other probability methods are adopted for the lower and upper bounds of the mean FHT-OS. The theory of a non-negative matrix, including the Perron-Frobenius theorem, the Jordan standard form, etc., are applied for the convergence of EAs. In addition, some techniques for time complexity and convergence of general adaptive EAs are also involved in this paper. Finally, the theoretical results obtained in this paper are verified effectively by applying them to the analysis of a typical evolutionary algorithm, (1 + 1)-EA.

关键词： Drift analysis Dynkin's formula evolutionary algorithms first hitting time Markov chain revised spectral time complexity

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EFFECTS OF ADAPTIVE SOCIAL NETWORKS ON THE ROBUSTNESS OF evolutionary algorithms

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2011年第5期20卷 783-817页

作者： Whitacre, James M. Sarker, Ruhul A. Pham, Q. Tuan Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ New S Wales Australian Def Force Acad Sch Informat Technol & Elect Engn Canberra ACT 2600 Australia Univ New S Wales Sch Chem Sci & Engn Sydney NSW 2052 Australia

Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the influence of component fitness and community cohesion (modularity) have attracted interest from the scientific community. In this study, we apply these concepts to an evolutionary algorithm and allow its population to self-organize using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based topological operators for guiding network structural dynamics, which in turn are guided by population changes taking place over evolutionary time. To investigate the effect on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and panmictic evolutionary algorithm designs. Our results suggest that a self-organizing topology evolutionary algorithm can exhibit robust search behavior with strong performance observed over short and long time scales. More generally, the coevolution between a population and its topology may constitute a promising new paradigm for designing adaptive search heuristics.

关键词： evolutionary algorithms network evolution optimization adaptive population topology self-organization

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The balance between proximity and diversity in multiobjective evolutionary algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2003年第2期7卷 174-188页

作者： Bosman, PAN Thierens, D Univ Utrecht Inst Informat & Comp Sci NL-3508 TB Utrecht Netherlands

Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for solving multiobjective optimization problems. Especially more recent multiobjective evolutionary algorithms (MOEAs) have been shown to be efficient and superior to earlier approaches. In the development of new MOEAs, the strive is to obtain increasingly better performing MOEAs. An important question however is whether we can expect such improvements to converge onto a specific efficient MOEA that behaves best on a large variety of problems. The best MOEAs to date behave similarly or are individually preferable with respect to different performance indicators. In this paper, we argue that the development of new MOEAs cannot converge onto a single new most efficient MOEA because the performance of MOEAs shows characteristics of multiobjective problems. While we will point out the most important aspects for designing competent MOEAs in this paper, we will also indicate the inherent multiobjective tradeoff in multiobjective optimization between proximity and diversity preservation. We will discuss the impact of this tradeoff on the concepts and design of exploration and exploitation operators. We also present a general framework for competent MOEAs and show how current state-of-the-art MOEAs can be obtained by making choices within this framework. Furthermore, we show an example of how we can separate nondomination selection pressure from diversity preservation selection pressure and discuss the impact of changing the ratio between these components.

关键词： density estimation diversity evolutionary algorithms exploitation exploration multiobjective optimization proximity selection pressure

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A hybrid approach to learn with imbalanced classes using evolutionary algorithms

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LOGIC JOURNAL OF THE IGPL 2011年第2期19卷 293-303页

作者： Milare, Claudia R. Batista, Gustavo E. A. P. A. Carvalho, Andre C. P. L. F. Univ Sao Paulo Inst Ciencias Matemat & Comp Sao Carlos SP Brazil

There is an increasing interest in the application of evolutionary algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

关键词： evolutionary algorithms treatment of imbalanced classes

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How Much Do Swarm Intelligence and evolutionary algorithms Improve Over a Classical Heuristic From 1960?

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IEEE ACCESS 2023年 11卷 19775-19793页

作者： Piotrowski, Adam P. Napiorkowski, Jaroslaw J. Piotrowska, Agnieszka E. Polish Acad Sci Inst Geophys PL-01452 Warsaw Poland Univ Warsaw Fac Polish Studies PL-00927 Warsaw Poland

Hundreds of variants of Swarm Intelligence or evolutionary algorithms are proposed each year and numerous competitions and comparisons between algorithms may suggest rapid improvement in the field. However, such comparisons are often done between a limited number of methods and are based on averaged ranks of algorithms. This way they measure whether one method is on average ranked better than the others, without giving any information on how much improvement is in fact obtained. In this study we show a general comparison between 69 algorithms, starting from methods proposed in the 1960's up to variants developed in the early 2020's, on single-objective static numerical problems. algorithms are compared on searching for a minimum of 30 different 50-dimensional mathematical functions, and on 22 real-world problems. We focus on the relative improvement achieved by various algorithms over a single-solution based method proposed in 1960 by Howard Rosenbrock. We find that the general improvement of evolutionary algorithms over Rosenbrock's algorithm is relatively limited. It is high for the artificial benchmarks, for which many evolutionary algorithms find solutions 10 times closer to the global optimum in terms of fitness than Rosenbrock's algorithm, but much lower for real-world problems. Improvement is also higher when performance averaged over many runs is compared, but lower when the best results from multiple runs are analyzed. In the last case, only the best evolutionary algorithms are able to find solutions of a "typical " real-world problem that are 2-3 times better in terms of fitness than those found by Rosenbrock's algorithm. The relative improvement of recently proposed algorithms is not much better than the improvement achieved by algorithms proposed over a decade ago.

关键词： Particle swarm optimization Optimization Benchmark testing Heuristic algorithms Metaheuristics Search problems Linear programming evolutionary algorithms swarm intelligence metaheuristics performance Rosenbrock-s algorithm

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Optimal acceleration autopilot design for non-minimum phase missiles using evolutionary algorithms

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2016年第4期8卷 221-227页

作者： Bijani, Vahid Khosravi, Alireza Sarhadi, Pouria Babol Noshirvani Univ Technol Dept Elect & Comp Engn Babol Sar Mazandaran Iran

The flight control system is a key element to achieve required performance in missiles and aircrafts. First purpose of flight control system is to ensuring the stability of the system, then, it attempts to force it to track the guidance commands. This paper provides a straightforward method using evolutionary optimisation algorithms to design an appropriate autopilot for non-minimum phase missiles. In order to bring the results to the actual conditions, the missile non-minimum phase model and actuator dynamics with time delay is considered. Proper indices such as system speed, overshoot, undershoot, steady state error and control signal effort have been incorporated to propose an innovative cost function. Then, several applicable meta-heuristic techniques are employed to optimise this cost function. Genetic algorithm, particle swarm optimisation, artificial bee colony, imperialist competitive algorithm and cuckoo search techniques have been compared in this optimisation problem. Simulation results on two benchmark problem show that this method has acceptable speed and it can be used in gain scheduling control design method for non-minimum phase systems. This method can be a suitable replacement for the time consuming procedure of gain tuning in gain scheduling method. The superior advantage of this method compared to the other methods is automatic tuning of the autopilot gains.

关键词： autopilot evolutionary algorithms non-minimum phase systems controller design

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On the evaluation of mono-nanofluids' ' density using a radial basis function neural network optimized by evolutionary algorithms

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 53卷

作者： Deymi, Omid Rezaei, Farzaneh Atashrouz, Saeid Nedeljkovic, Dragutin Mohaddespour, Ahmad Hemmati-Sarapardeh, Abdolhossein Shahid Bahonar Univ Kerman Dept Mech Engn Kerman Iran Amirkabir Univ Technol Tehran Polytech Dept Petr Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Dept Chem Engn Tehran Iran Amer Univ Middle East Coll Engn & Technol Kuwait Kuwait McGill Univ Dept Chem Engn Montreal PQ H3A 0C5 Canada Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran China Univ Petr State Key Lab Petr Resources & Prospecting Beijing Peoples R China

The interest in understanding nanofluid density's impact on heat transfer and fluid flow behaviors has driven the need for accurate density values. Artificial intelligence techniques for predicting nanofluid density provide a cost-effective and efficient alternative to labor-intensive lab experiments. In the current research, four distinct models based on the Radial Basis Function (RBF) Neural Network were developed and implemented on an extensive and comprehensive databank comprising 4004 experimental data-points gathered from multiple available sources. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA), and Genetic Algorithm (GA) were used separately to optimize the neural network. The provided databank introduces 95 varieties of mono-nanofluids, including 16 types of nano-particles and 11 types of base- fluids. The target/dependent variable in this research is the density of mono-nanofluids (rho nf), whereas the input/ independent variables include the average nano-particle diameter (dnp), nano-particle mass concentration (phi m), temperature (T), pressure (P), nano-particle density (rho np), and base-fluid density (rho bf). Various analyses of the four models confirmed the RBF-ACO model's robustness and superiority. Key statistical indicators for this model indicated an Average Absolute Percent Relative Error (AAPRE) of 0.5391%, a Standard Deviation (SD) of 0.0099, and a Coefficient of Determination (R2) of 0.9818. Sensitivity analysis for the superior model identified key input-output variables with high relevancy factors (r-values). Notably, variables phi m and rho bf had maximum rvalues close to 0.70, indicating their significant role in predicting mono-nanofluids' density. In addition, a leverage statistical approach was utilized to determine possible outliers and the applicability domain of the RBFACO model.

关键词： Mono-nanofluids Density RBF neural network evolutionary algorithms Statistical indicators Relevancy factors

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Optimizing neotissue growth inside perfusion bioreactors with respect to culture and labor cost: a multi-objective optimization study using evolutionary algorithms

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2020年第7期23卷 285-294页

作者： Mehrian, Mohammad Geris, Liesbet Univ Liege GIGA In Silico Med Biomech Res Unit Liege Belgium Katholieke Univ Leuven Div Skeletal Tissue Engn Prometheus Leuven Belgium Katholieke Univ Leuven Biomech Sect Leuven Belgium

Tissue engineering is a fast progressing domain where solutions are provided for organ failure or tissue damage. In this domain, computer models can facilitate the design of optimal production process conditions leading to robust and economically viable products. In this study, we use a previously published computationally efficient model, describing the neotissue growth (cells + their extracellular matrix) inside 3D scaffolds in a perfusion bioreactor. In order to find the most cost-effective medium refreshment strategy for the bioreactor culture, a multi-objective optimization strategy was developed aimed at maximizing the neotissue growth while minimizing the total cost of the experiment. Four evolutionary optimization algorithms (NSGAII, MOPSO, MOEA/D and GDEIII) were applied to the problem and the Pareto frontier was computed in all methods. All algorithms led to a similar outcome, albeit with different convergence speeds. The simulation results indicated that, given the actual cost of the labor compared to the medium cost, the most cost-efficient way of refreshing the medium was obtained by minimizing the refreshment frequency and maximizing the refreshment amount.

关键词： Multi-objective optimization evolutionary algorithms bone tissue engineering 3D scaffold labor cost

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