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Eigenfrequency-based topology optimization using cooperative coevolutionary strategies and moving morphable components

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第5期44卷 1-26页

作者： Rostami, Pooya Marzbanrad, Javad Parsa, Mohammad Hossein Taghavi Iran Univ Sci & Technol Sch Automot Engn Vehicle Dynam Syst Res Lab Tehran Iran Univ Qom Fac Engn Dept Civil Engn Qom Iran

This paper aims to present and investigate a derivative-free design optimization methodology for continuum structures to maximize the eigenfrequencies with different constraints. The recently developed moving morphable component method is used as the parameterization technique which is so suitable for non-gradient optimization algorithms due to the lower numbers of design variables. Two objective functions are considered in this paper. In the first point of view, the aim is to maximize multiple eigenfrequencies. For the second strategy, the maximization of multiple frequency gaps is considered. Multiple constraints are considered in the problem definition. Instead of virtual mass, static stress and compliance are defined. Previous papers presented optimal designs only for eigenfrequency maximization and used a virtual mass to prevent discontinuity in the design domain. Herein, the topological design is performed for both static (stress and compliance) and eigenfrequency targets. Based on the results, the cooperative coevolutionary strategy coupled with MMC has a very good potential in solving eigenfrequency-related problems while needing no sensitivity analysis. Since this algorithm is so easy to implement and very successful in benchmark problems, industries can use it even for more complex cases. Also, it is observed that the algorithm can produce diverse and competitive outputs due to non-deterministic behavior. So it can be categorized as generative design tools.

关键词： Topology optimization Eigenfrequency Stress constraint Moving morphable components evolutionary computations Generative design

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An M/M/c queue model for hub covering location problem

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MATHEMATICAL AND COMPUTER MODELLING 2011年第11-12期54卷 2623-2638页

作者： Mohammadi, Mehrdad Jolai, Fariborz Rostami, Hamideh Univ Tehran Dept Ind Engn Coll Engn Tehran Iran Univ Tabriz Dept Ind Engn Coll Engn Tabriz Iran

The hub location problem appears in a variety of applications including airline systems, cargo delivery systems, and telecommunication network design. Hub location problems deal with finding the location of hub facilities and the allocation of demand nodes to these located hub facilities. We consider a hub-and-spoke network problem with crowdedness or congestion in the system. The transportation time and the rate of arrived trucks to each hub are random variables. In addition, a hub cannot service all trucks simultaneously and it has some restrictions like capacity constraint and the service time limitations. Hubs, which are the most crowded parts of network, are modeled as M/M/c queuing systems. In the application of the proposed model for a cargo transportation system, the number of trucks follows Poisson probability distribution in the queuing system. In this paper at first a nonlinear mathematical programming is presented to find an optimal solution for the considered problem. A probabilistic constraint is included to ensure that the probability of b trucks in a queue is less than a threshold value. for each hub. Then, we transfer the introduced nonlinear constraints of the mathematical programming model to the linear constraints. Due to the computational complexity of the resulted model, we propose an improved meta-heuristic based on Imperialist Competitive Algorithm and Genetic Algorithm to find near optimal solution of the problem. The performance of the solutions found by the proposed improved meta-heuristic is compared with those of pure GA and those of the mathematical programming model. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Location Facilities planning and design Hub covering location Queuing evolutionary computations Imperialist Competitive Algorithm

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Multi-objective mean-variance-skewness model for nonconvex and stochastic optimal power flow considering wind power and, load uncertainties

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期263卷 719-732页

作者： Chen, J. J. Wu, Q. H. Zhang, L. L. Wu, P. Z. South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Guangdong Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Univ Liverpool Dept Phys Liverpool L69 3GJ Merseyside England

This paper presents a multi-objective mean variance-skewness (MVS) model for reliably assessing the problem of nonconvex and stochastic optimal power flow (NSOPF), considering valve-point effect, stochastic load and uncertain integrated wind power. The MVS model aims to maximize both the expected return and skewness while simultaneously minimizing the risk, which is formulated as a competing and conflicting three-objective optimization problem. Based on such a model, we propose a multi objective optimization algorithm, multiple preys based evolutionary predator and prey strategy (MPEPPS), to provide Pareto solutions, which show the trade-off relationship among the expected return, the skewness and the risk of the dispatching objective. Subsequently, a multi-criteria decision making method, the technique for order preference by similarity to an ideal solution (TOPSIS), is applied for determining the final dispatch solution. The objective of this paper is to develop a reliable model to assess the NSOPF from the perspective of economics and reliability of power system operation, and propose an efficient algorithm to obtain a solution that considers all of the possible load and wind power simultaneously. Simulation results based on a modified IEEE 30-bus power system demonstrate the reliability and effectiveness of the MVS and MPEPPS in solving NSOPF. (c) 2017 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Mean-variance-skewness model Optimal power flow Uncertainties Multi-objective optimization

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Influence of encoding and neighborhood in landscape analysis and tabu search performance for job shop scheduling problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期319卷 739-746页

作者： Tsogbetse, Israel Bernard, Julien Manier, Herve Manier, Marie-Ange UTBM CNRS FEMTO ST F-90010 Belfort France Univ Franche Comte CNRS FEMTO ST F-25000 Besancon France

Fitness landscape analysis is used to understand search spaces of combinatorial problems that can hardly be solved exactly, such as the job shop scheduling problem. We analyze the influence of the solution encodings and the neighborhood operators on usual metrics for landscape analysis, and try to correlate the results to the performance of a local search method such as tabu search using these encodings and operators for a wide range of instances of the job shop scheduling problem.

关键词： Scheduling Metaheuristics evolutionary computations

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Scheduling electric power production at a wind farm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第1期224卷 227-238页

作者： Zhang, Zijun Kusiak, Andrew Song, Zhe Nanjing Univ Sch Business Nanjing 210093 Jiangsu Peoples R China Univ Iowa Dept Mech & Ind Engn Seamans Ctr 3131 Iowa City IA 52242 USA City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China

We present a model for scheduling power generation at a wind farm, and introduce a particle swarm optimization algorithm with a small world network structure to solve the model. The solution generated by the algorithm defines the operational status of wind turbines for a scheduling horizon selected by a decision maker. Different operational scenarios are constructed based on time series data of electricity price, grid demand, and wind speed. The computational results provide insights into management of a wind farm. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Scheduling evolutionary computations Wind farm Particle swarm optimization Small world network

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Using variable reduction strategy to accelerate evolutionary optimization

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APPLIED SOFT COMPUTING 2017年 61卷 283-293页

作者： Wu, Guohua Pedrycz, Witold Suganthan, P. N. Li, Haifeng Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Cent S Univ Sch Geosci & Infophys Changsha 410004 Hunan Peoples R China

In this study, we introduce a novel approach of variable reduction and integrate it into evolutionary algorithms in order to reduce the complexity of optimization problems. We develop reduction processes of variable reduction for derivative unconstrained optimization problems (DUOPs) and constrained optimization problems (COPs) with equality constraints and active inequality constraints. Variable reduction uses the problem domain knowledge implied when investigating optimal conditions existing in optimization problems. For DUOPs, equations involving derivatives are considered while for COPs, we discuss equations expressing the equality constraints. From the relationships formed in this way, we obtain relationships among the variables that have to be satisfied by optimal solutions. According to such relationships, we can utilize some variables (referred to as core variables) to express some other variables (referred to as reduced variables). We show that the essence of variable reduction is to produce a minimum collection of core variables and a maximum number of reduced variables based on a system of equations. We summarize some application-oriented situations of variable reduction and stress several important issues related to the further application and development of variable reduction. Essentially, variable reduction can reduce the number of variables and eliminate equality constraints, thus reducing the dimensionality of the solution space and improving the efficiency of evolutionary algorithms. The approach can be applied to unconstrained, constrained, continuous and discrete optimization problems only if there are explicit variable relationships to be satisfied in the optimal conditions. We test variable reduction on real-world and synthesized DUOPs and COPs. Experimental results and comparative studies point at the effectiveness of variable reduction. (C) 2017 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Unconstrained optimization Constrained optimization Variable reduction Complexity reduction

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Genetic regulatory network-based symbiotic evolution

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4756-4773页

作者： Hu, Jhen-Jia Li, Tzuu-Hseng S. Natl Cheng Kung Univ Dept Elect Engn AiRobots Lab Tainan 701 Taiwan

The main theme of this paper is to present a novel evolution, the genetic regulatory network-based symbiotic evolution (GRNSE), to improve the convergent speed and solution accuracy of genetic algorithms. The proposed GRNSE utilizes genetic regulatory network (GRN) reinforcement learning to improve the diversity and symbiotic evolution (SE) initialization to achieve the parallelism. In particular, GRN-based learning increases the global rate by regulating members of genes in symbiotic evolution. To compare the efficiency of the proposed method, we adopt 41 benchmarks that contain many nonlinear and complex optimal problems. The influences of dimension, individual population size, and gene population size are examined. A new control parameter, the population rate is introduced to initiate the ratio between the gene and chromosome. Finally, all the studies of there 41 benchmarks demonstrate that from the statistic point of view, GRNSE give a better convergence speed and a more accurate optimal solution than GA and SE. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.

关键词： evolutionary computations Genetic algorithm Genetic regulatory network Global optimization problem Reinforcement learning Symbiotic evolution

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Optimum design of shell structures with random geometric, material and thickness imperfections

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INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES 2006年第22-23期43卷 6948-6964页

作者： Lagaros, Nikos D. Papadopoulos, Vissarion Natl Tech Univ Athens Inst Struct Anal & Seism Res Athens 15780 Greece

The optimum design of isotropic shell structures with random initial geometric, material and thickness imperfections is investigated in this paper and a robust and efficient methodology is presented for treating such problems. For this purpose, the concept of an initial "imperfect" structure is introduced involving not only geometric deviations of the shell structure from its perfect geometry but also a spatial variability of the modulus of elasticity as well as of the thickness of the shell. An efficient reliability-based design optimization (RBDO) formulation is proposed. The objective function is considered to be the weight of the structure while both deterministic and probabilistic constraints are taken into account. The overall probability of failure is taken as the global probabilistic constraint for the optimization procedure. Numerical results are presented for a cylindrical panel, demonstrating the efficiency as well as the applicability of the proposed methodology in obtaining rational optimum designs of imperfect shell-type structures. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： evolutionary computations reliability-based design optimization nonlinear shell finite element random imperfections spectral representation

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A hybrid evolutionary algorithm for the two-dimensional packing problem

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2014年第4期22卷 729-753页

作者： Kierkosz, Igor Luczak, Maciej Koszalin Univ Technol Dept Civil & Environm Engn PL-75453 Koszalin Poland

This paper presents a hybrid evolutionary algorithm for the two-dimensional non-guillotine packing problem. The problem consists of packing many rectangular pieces into a single rectangular sheet in order to maximize the total area of the pieces packed. Moreover, there is a constraint on the maximum number of times that a piece may be used in a packing pattern. The set of packing patterns is processed by an evolutionary algorithm. Three mutation operators and two types of quality functions are used in the algorithm. The best solution obtained by the evolutionary algorithm is used as the initial solution in a tree search improvement procedure. This approach is tested on a set of benchmark problems taken from the literature and compared with the results published by other authors.

关键词： Packing Cutting evolutionary computations Metaheuristics

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Working principles, behavior, and performance of MOEAs on MNK-landscapes

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期181卷 1670-1690页

作者： Aguirre, Hernan E. Tanaka, Kiyoshi Shinshu Univ Fac Engn Nagano 3808553 Japan

This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs. (C) 2006 Elsevier B.V. All rights reserved.

关键词： evolutionary computations multiobjective evolutionary algorithms multiobjective combinatorial optimization MNK-landscapes epistasis non-linear multiobjective fitness functions discrete binary search spaces selection drift mutation recombination

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