A Comparison of Numerical Optimization Methods for Engineering Design

1974 ◽  
Vol 96 (1) ◽  
pp. 196-200 ◽  
Author(s):  
E. D. Eason ◽  
R. G. Fenton
Keyword(s):  
Engineering Design ◽  
Numerical Optimization ◽  
Optimization Methods ◽  
Test Functions ◽  
Design Problems

Seventeen numerical optimization methods are compared by plotting their convergence characteristics when applied to design problems and test functions. Several ranking schemes are used to determine the most general, efficient, inexpensive, and convenient methods. Conclusions are presented in the form of a selection guide intended for general use.

The Complementary Roles of Symbolic Computing and Numerical Optimization in Engineering Design Software

1987 ◽  
Author(s):  
R. Ellsworth ◽  
A. Parkinson ◽  
F. Cain
Keyword(s):  
Expert System ◽  
Engineering Design ◽  
Numerical Optimization ◽  
Optimization Methods ◽  
Design Rule ◽  
Rule Base ◽  
Design Problems ◽  
Symbolic Computing ◽  
Engineering Design Problems ◽  
Trial And Error Method

Abstract In many engineering design problems, the designer converges upon a good design by iteratively evaluating a mathematical model of the design problem. The trial-and-error method used by the designer to converge upon a solution may be complex and difficult to capture in an expert system. It is suggested that in many cases, the design rule base could be made significantly smaller and more maintainable by using numerical optimization methods to identify the best design. The expert system is then used to define the optimization problem and interpret the solution, as well as to apply the true heuristics to the problem. An example of such an expert system is presented for the design of a valve anti-cavitation device. Because of the capabilities provided by the optimization software, the expert system has been able to outperform the expert in the test cases evaluated so far.

The Complementary Roles of Knowledge-Based Systems and Numerical Optimization in Engineering Design Software

1989 ◽  
Vol 111 (1) ◽  
pp. 100-103 ◽  
Author(s):  
R. Ellsworth ◽  
A. Parkinson ◽  
F. Cain
Keyword(s):  
Expert System ◽  
Engineering Design ◽  
Numerical Optimization ◽  
Optimization Methods ◽  
Design Rule ◽  
Rule Base ◽  
Design Problems ◽  
Knowledge Based ◽  
Engineering Design Problems ◽  
Trial And Error Method

In many engineering design problems, the designer converges upon a good design by iteratively evaluating a mathematical model of the design problem. The trial-and-error method used by the designer to converge upon a solution may be complex and difficult to capture in an expert system. It is suggested that in many cases, the design rule base could be made significantly smaller and more maintainable by using numerical optimization methods to identify the best design. The expert system is then used to define the optimization problem and interpret the solution, as well as to apply the true heuristics to the problem. An example of such an expert system is presented for the design of a valve anticavitation device. Because of the capabilities provided by the optimization software, the expert system has been able to outperform the expert in the test cases evaluated so far.

scholarly journals An Improved Butterfly Optimization Algorithm for Engineering Design Problems Using the Cross-Entropy Method

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1049 ◽  
Author(s):  
Guocheng Li ◽  
Fei Shuang ◽  
Pan Zhao ◽  
Chengyi Le
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Global Search ◽  
Cross Entropy ◽  
Local Optimum ◽  
Test Functions ◽  
Design Problems ◽  
Cross Entropy Method ◽  
Engineering Design Problems ◽  
The Cross

Engineering design optimization in real life is a challenging global optimization problem, and many meta-heuristic algorithms have been proposed to obtain the global best solutions. An excellent meta-heuristic algorithm has two symmetric search capabilities: local search and global search. In this paper, an improved Butterfly Optimization Algorithm (BOA) is developed by embedding the cross-entropy (CE) method into the original BOA. Based on a co-evolution technique, this new method achieves a proper balance between exploration and exploitation to enhance its global search capability, and effectively avoid it falling into a local optimum. The performance of the proposed approach was evaluated on 19 well-known benchmark test functions and three classical engineering design problems. The results of the test functions show that the proposed algorithm can provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence rate. The results of the engineering problems prove that the new approach is applicable to challenging problems with constrained and unknown search spaces.

scholarly journals Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

2020 ◽  
Vol 13 (6) ◽  
pp. 279-293
Author(s):  
Hanan Akkar ◽  
◽  
Sameem Salman ◽  
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Optimization Methods ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Minimum Number ◽  
Grasshopper Optimization ◽  
Design Pressure

This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

Formulation of Multicriterion Design Optimization Problems for Solution With Scalar Numerical Optimization Methods

2004 ◽  
Vol 48 (01) ◽  
pp. 61-76 ◽  
Author(s):  
Michael G. Parsons ◽  
Randall L. Scott
Keyword(s):  
Design Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Pareto Optimum ◽  
Compromise Solution ◽  
Design Problems ◽  
Common Definition ◽  
Bulk Carriers ◽  
Definition Of

Most marine design problems involve multiple conflicting criteria, objectives, or goals. The most common definition of the multicriterion optimum is the Pareto optimum, which usually results in a set of solutions. Design teams, however, need to arrive at a single answer that provides an acceptable compromise solution within the Pareto set. Methods have been developed to solve multicriterion optimization problems using a number of related definitions of the compromise solution or "optimum" in the presence of multiple conflicting criteria. The most common of these definitions are reviewed and their solutions are formulated in a consistent form utilizing a preference function that will allow their solution using conventional scalar criterion numerical optimization methods. This approach permits the use and comparison of the various definitions of the multicriterion "optimum" with modest additional computation. The design team can use these results to guide its selection of the solution that best reflects their design intent in a particular case. A sixparameter, three-criterion, 14-to 16-constraint conceptual marine design optimization example adapted from the literature is presented to illustrate the use of this approach. The results for the various definitions of the multicriterion optimum for Panamax and post-Panamax bulk carriers are presented for comparison.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

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