scholarly journals Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

2021 ◽  
Vol 12 (1) ◽  
pp. 407
Author(s):  
Tianshan Dong ◽  
Shenyan Chen ◽  
Hai Huang ◽  
Chao Han ◽  
Ziqi Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Approximation Accuracy ◽  
Design Variables ◽  
Metaheuristic Methods ◽  
Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

scholarly journals An Improved Genetic Algorithm for the Optimization of Composite Structures

2006 ◽  
Author(s):  
Vladimir Gantovnik ◽  
Georges Fadel ◽  
Zafer Gu¨rdal
Keyword(s):  
Genetic Algorithm ◽  
Composite Structures ◽  
Optimization Problems ◽  
Multivariate Approximation ◽  
Approximation Procedure ◽  
Continuous Variables ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Proposed Modification

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed modification improves the efficiency of the memory constructed in terms of the continuous variables. The work presents the algorithmic implementation of the proposed memory scheme and demonstrates the efficiency of the proposed multivariate approximation procedure for the weight optimization of a segmented open cross section composite beam subjected to axial tension load. Results are generated to demonstrate the advantages of the proposed improvements to a standard genetic algorithm.

scholarly journals Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 297 ◽  
Author(s):  
Ke Shen ◽  
Toon Pessemier ◽  
Xu Gong ◽  
Luc Martens ◽  
Wout Joseph
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Production ◽  
Optimal Schedule ◽  
Memory Search ◽  
Search Problem ◽  
Scheduling Problems ◽  
Improved Genetic Algorithm

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA.

scholarly journals Improved Genetic Algorithm with Two-Level Approximation for Truss Optimization by Using Discrete Shape Variables

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shen-yan Chen ◽  
Xiao-fang Shui ◽  
Dong-fang Li ◽  
Hai Huang
Keyword(s):  
Genetic Algorithm ◽  
Computational Cost ◽  
Approximation Problem ◽  
Optimization Method ◽  
Truss Optimization ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Fitness Value ◽  
Update Strategy

This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and genetic algorithm (GA), a new method for adding shape variables is presented, in which the nodal positions are corresponding to a set of coordinate lists. A uniform optimization model including size/shape/topology variables is established. First, a first-level approximate problem is constructed to transform the original implicit problem to an explicit problem. To solve this explicit problem which involves size/shape/topology variables, GA is used to optimize individuals which include discrete topology variables and shape variables. When calculating the fitness value of each member in the current generation, a second-level approximation method is used to optimize the continuous size variables. With the introduction of shape variables, the original optimization algorithm was improved in individual coding strategy as well as GA execution techniques. Meanwhile, the update strategy of the first-level approximation problem was also improved. The results of numerical examples show that the proposed method is effective in dealing with the three kinds of design variables simultaneously, and the required computational cost for structural analysis is quite small.

scholarly journals Air Combat Maneuver Decision Based on Reinforcement Genetic Algorithm

2020 ◽  
Vol 38 (6) ◽  
pp. 1330-1338
Author(s):  
Jianfeng Xie ◽  
Qiming Yang ◽  
Shuling Dai ◽  
Wanyang Wang ◽  
Jiandong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Engineering Application ◽  
Improved Genetic Algorithm ◽  
Test Scenario ◽  
Decision Sequence ◽  
Air Combat ◽  
Scale Optimization ◽  
Searching Ability

With the continuous development of UAV technology, the trend of using UAV in the military battlefield is increasingly obvious, but the autonomous air combat capability of UAV needs to be further improved. The air combat maneuvering decision is the key link to realize the UAV autonomous air combat, and the genetic algorithm has good robustness and global searching ability which is suitable for solving large-scale optimization problems. This paper uses an improved genetic algorithm to model UAV air combat maneuvering decisions. Based on engineering application requirements, a typical simulation test scenario is established. The simulation results show that the air combat maneuvering decision model based on reinforcement genetic algorithm in this paper can obtain the correct maneuvering decision sequence and gain a position advantage in combat.

Optimization of Stirling Engine Performance Based on Multipoint Approximation Technique

1994 ◽  
Author(s):  
Vassili V. Toropov ◽  
Henrik Carlsen
Keyword(s):  
Computing Time ◽  
Engine Performance ◽  
Stirling Engine ◽  
Optimization Techniques ◽  
Approximation Technique ◽  
Working Cycle ◽  
Design Variables ◽  
Stirling Engines ◽  
Multipoint Approximation ◽  
Conventional Optimization

Abstract The ideal Stirling working cycle has the maximum obtainable efficiency defined by Carnot efficiency, and highly efficient Stirling engines can therefore be built, if designed properly. To analyse the power output and the efficiency of a Stirling engine, numerical simulation programs (NSP) have been developed, which solve the thermodynamic equations. In order to find optimum values of design variables, numerical optimization techniques can be used (Bartczak and Carlsen, 1991). To describe the engine realistically, it is necessary to consider several tens of design variables. As even a single call for NSP requires considerable computing time, it would be too time consuming to use conventional optimization techniques, which require a very large number of calls for NSP. Furthermore, objective and constraint functions of the optimization problem present some level of noise, i.e. can only be estimated with a finite accuracy. To cope with these problems, the multipoint explicit approximation technique is used.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

scholarly journals A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 758
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Space ◽  
Large Scale Problems ◽  
Processing Power ◽  
Compact Genetic Algorithm ◽  
Estimation Of Distribution ◽  
Feasible Solutions ◽  
Compact Optimization

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and material engineering, networked systems, logistics and scheduling. Recently, Deb and Myburgh proposed an evolutionary algorithm capable of handling a scheduling optimization problem with a staggering number of variables: one billion. However, one important limitation of this algorithm is its memory consumption, which is in the order of 120 GB. Here, we follow up on this research by applying to the same problem a GPU-enabled “compact” Genetic Algorithm, i.e., an Estimation of Distribution Algorithm that instead of using an actual population of candidate solutions only requires and adapts a probabilistic model of their distribution in the search space. We also introduce a smart initialization technique and custom operators to guide the search towards feasible solutions. Leveraging the compact optimization concept, we show how such an algorithm can optimize efficiently very large-scale problems with millions of variables, with limited memory and processing power. To complete our analysis, we report the results of the algorithm on very large-scale instances of the OneMax problem.

Optimization for Crashworthiness of Urban Transit Trains Using Genetic Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1167-1172 ◽  
Author(s):  
Zhuo Jun Xie ◽  
Ping Xu ◽  
Yu Qi Luo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Urban Transit ◽  
Design Variables ◽  
Specific Goal ◽  
Design Function ◽  
Crash Model ◽  
Current Energy ◽  
Force Characteristics

As it is tough for the current energy absorb devices of urban vehicles to meet the crashworthiness requirements in the collision scenario of 25km/h, a methodology to improve the general crashworthiness is presented. A multi-criteria optimization, with the deformations and accelerations of all cars as the design functions and the force characteristics of end structures of cars as design variables, is defined and the Pareto Fonts are obtained. Then defining energy absorbed as design function, a single criteria optimization is made and the specific goal is achieved. No explicit relationship could be found between the design variables and the design functions, so a crash model of a train with velocity of 25km/h colliding to another train stopped is built and the genetic algorithm is chosen to solve the optimization problems. The results indicate that the crashworthiness performance of the trains is significantly improved and the crashworthiness requirements could be reached finally.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

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