Global optimization of truss topology with discrete bar areas—Part II: Implementation and numerical results

2007 ◽  
Vol 44 (2) ◽  
pp. 315-341 ◽  
Author(s):  
Wolfgang Achtziger ◽  
Mathias Stolpe
Keyword(s):  
Global Optimization ◽  
Numerical Results ◽  
Truss Topology

scholarly journals A Global Optimization Approach to Solve Multi-Aircraft Routing Problems

2010 ◽  
pp. 237-259
Author(s):  
S.P. Wilson ◽  
M.C. Bartholomew-Biggs ◽  
S.C. Parkhurst
Keyword(s):  
Global Optimization ◽  
Real Life ◽  
Two Dimensions ◽  
Numerical Results ◽  
Solution Technique ◽  
Optimization Approach ◽  
Routing Problem ◽  
Aircraft Routing ◽  
Optimization Calculation ◽  
Multiple Routes

This chapter describes the formulation and solution of a multi-aircraft routing problem which is posed as a global optimization calculation. The chapter extends previous work (involving a single aircraft using two dimensions) which established that the algorithm DIRECT is a suitable solution technique. The present work considers a number of ways of dealing with multiple routes using different problem decompositions. A further enhancement is the introduction of altitude to the problems so that full threedimensional routes can be produced. Illustrative numerical results are presented involving up to three aircraft and including examples which feature routes over real-life terrain data.

Differential Operators Embedded Artificial Bee Colony Algorithm

2013 ◽  
pp. 149-163
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Differential Operators ◽  
Optimization Problems ◽  
Numerical Results ◽  
Benchmark Problems ◽  
Continuous Space ◽  
Abc Algorithm ◽  
Bee Colony

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

Differential Operators Embedded Artificial Bee Colony Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Differential Operators ◽  
Optimization Problems ◽  
Numerical Results ◽  
Benchmark Problems ◽  
Continuous Space ◽  
Abc Algorithm ◽  
Bee Colony

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

A novel global optimization method of truss topology

2011 ◽  
Vol 54 (10) ◽  
pp. 2723-2729 ◽  
Author(s):  
Qi Wang ◽  
ZhenZhou Lu ◽  
ZhangChun Tang
Keyword(s):  
Global Optimization ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Truss Topology

SOLVING CONSTRAINED GLOBAL OPTIMIZATION VIA ARTIFICIAL IMMUNE SYSTEM

2011 ◽  
Vol 20 (01) ◽  
pp. 1-27 ◽  
Author(s):  
JUI-YU WU
Keyword(s):  
Bone Marrow ◽  
Global Optimization ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Numerical Results ◽  
Network Selection ◽  
Receptor Editing ◽  
Idiotypic Network ◽  
Artificial Immune ◽  
Immune Systems

Artificial immune systems (AISs) are computational intelligence (CI) oriented methods using information based on biological immune systems. In this study, an AIS, which combines the metaphor of clonal selection with idiotypic network theories, is developed. Although they are contradictory approaches, clonal selection and idiotypic network may prove useful in designing a stochastic global optimization tool. The AIS method consists of idiotypic network selection, somatic hypermuation, receptor editing and bone marrow operators. The idiotypic network selection operator determines the number of good solutions. The somatic hypermutation and receptor editing operators comprise the searching mechanisms for the exploration of the solution space. Diversity on the population of solutions is ensured by the bone marrow operator. The performance of the proposed AIS method is tested on a set of global constrained optimization problems (GCO), comprising of four benchmark nonlinear programming problems and four generalized polynomial programming (GPP) problems, where GPP problems are nonconvex optimization problems. The best solution found by the AIS algorithm is compared with the known global optimum. Numerical results show that the proposed method converged to the global optimal solution to each tested CGO problem. Moreover, this study compares the numerical results obtained by the AIS approach with those taken from published CI approaches, such as alternative AIS methods and genetic algorithms.

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