Applications of GA and GP to Industrial Design Optimization and Inverse Problems

Toropov, Vassili; Álvarez, Luis Fernando Zapata; Querin, Osvaldo M.

doi:10.1007/978-3-211-99768-0_3

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…where u is an offset constant and e w is the unit vector along the direction of wind. Substituting S, G, and A in equation (6), this equation can be expressed as follows:…”

Section: Algorithm Preliminariesmentioning

confidence: 99%

“…where ub d is the upper bound of the dth dimension, lb d is the lower bound of the dth dimension, s(r) � fe (− r/l) − e − r , T d is the value of dth dimension of the target (best solution found so far), and c is a decreasing coefficient of comfort zone, repulsion zone, and attraction zone, as defined in equation (13). Note that S is almost similar to the component S in equation (6). However, we do not consider the gravity (no G component) and assume that the wind direction is always toward a target (T d ).…”

Section: Algorithm Preliminariesmentioning

confidence: 99%

“…Check boundaries; (33) end for (34) end for (35) Select N number of the fittest individuals from X ∪ 6 Complexity…”

Section: Analysis Of Convergencementioning

confidence: 99%

“…Global optimization has become an important research topic in scientific research and engineering applications. Neural network training [1,2], path planning [3,4], industrial design [5,6], and many other complex problems hope to use optimization algorithms to achieve optimum solutions. Global optimization algorithms mainly consist of two classes: deterministic mathematical programming method [7][8][9][10] and metaheuristic algorithms (MAs) [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

et al. 2020

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Job shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. The recently proposed dynamic opposite learning (DOL) strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP.

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“…where u is an offset constant and e w is the unit vector along the direction of wind. Substituting S, G, and A in equation (6), this equation can be expressed as follows:…”

Section: Algorithm Preliminariesmentioning

confidence: 99%

Section: Algorithm Preliminariesmentioning

confidence: 99%

“…Check boundaries; (33) end for (34) end for (35) Select N number of the fittest individuals from X ∪ 6 Complexity…”

Section: Analysis Of Convergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

et al. 2020

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“…Search of so-called "pro-composite" concepts, i.e. the concepts allowing to realize high potential advantages of composite materials more efficiently than in frame of "Black metal" concept, is now considered by many scientific schools as one of perspective and promising directions in development of the next generation of airframes and space structures [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Lightweight Composite and Hybrid Primary Aircraft Structures

Shanygin¹

Civil-Comp Proceedings

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The main problems of weight saving for composite airframes are considered and substantiated in this paper. A new complex program for fast and confident composite structure weight estimation on the basis of the multilevel approach is described. The relationship between the composite aircraft structure weight and the physical characteristics of the resin was obtained on the basis of numerical and experimental results. An investigation of alternative "black metal" and "Frame" structure concepts for a flying wing aircraft has shown that the "frame" concept has more potential in weight saving for the resin currently available.

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Multidisciplinary Optimization of turbomachinary based on metamodel built by Genetic Programming

Polynkin

Toropov

Shaphar

2010

13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference

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Applications of GA and GP to Industrial Design Optimization and Inverse Problems

Cited by 3 publications

References 29 publications

A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

Investigation of Lightweight Composite and Hybrid Primary Aircraft Structures

Multidisciplinary Optimization of turbomachinary based on metamodel built by Genetic Programming

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