Classical optimization

1991 ◽  
pp. 65-69
Author(s):  
Peter Berck ◽  
Knut Sydsæter
Keyword(s):  
Classical Optimization

scholarly journals Classical Optimization of Cellulase and Xylanase Production by a Marine Streptomyces Species

2016 ◽  
Vol 6 (10) ◽  
pp. 286 ◽  
Author(s):  
Evelyn Fatokun ◽  
Uchechukwu Nwodo ◽  
Anthony Okoh
Keyword(s):  
Xylanase Production ◽  
Streptomyces Species ◽  
Marine Streptomyces ◽  
Classical Optimization

Classical optimization

1999 ◽  
pp. 87-94
Author(s):  
Knut Sydsæter ◽  
Arne Strøm ◽  
Peter Berck
Keyword(s):  
Classical Optimization

Afterword

2010 ◽  
pp. 269-272
Author(s):  
E. Parsopoulos Konstantinos ◽  
N. Vrahatis Michael
Keyword(s):  
Optimization Problems ◽  
Optimization Methods ◽  
Special Structure ◽  
Integer Optimization ◽  
Hybrid Schemes ◽  
Research Results ◽  
Global Minimizers ◽  
Classical Optimization

In the previous chapters, we presented the fundamental concepts and variants of PSO, as along with a multitude of recent research results. The reported results suggest that PSO can be a very useful tool for solving optimization problems from different scientific and technological fields, especially in cases where classical optimization methods perform poorly or their application involves formidable technical difficulties due to the problem’s special structure or nature. PSO was capable of addressing continuous and integer optimization problems, handling noisy and multiobjective cases, and producing efficient hybrid schemes in combination with specialized techniques or other algorithms in order to detect multiple (local or global) minimizers or control its own parameters.

scholarly journals Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 980
Author(s):  
Gustavo Meirelles ◽  
Bruno Brentan ◽  
Joaquín Izquierdo ◽  
Edevar Luvizotto
Keyword(s):  
Systems Engineering ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Sensitivity Analyses ◽  
Grand Tour ◽  
Agent Based ◽  
Local Optimal Solution ◽  
Classical Optimization

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.

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