Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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1 citations
01 Mar 1987
TL;DR: An optimization strategy is presented that provides a frame-work in which optimization algorithms and heuristic procedures can be coupled to solve nonlinearly constrained design optimization problems.
Abstract: An optimization strategy is presented that provides a frame-work in which optimization algorithms and heuristic procedures can be coupled to solve nonlinearly constrained design optimization problems These problems cannot be efficiently solved by either approach independently The approach is based on an optimization algorithm dealing with local monotonicity and sequential quadratic programming techniques with heuristic procedures which are statistically derived from observations obtained by applying the optimization algorithm to different classes of test problems
1 citations
01 Jan 2009
TL;DR: In this article, a real GA based approach was used to solve the Traveling Salesman Problem (TSP) in the field of solid waste routing system in the large cities.
Abstract: This work aims at solving the Traveling Salesman Problem (TSP) through developing an advanced intelligent technique based on real Genetic Algorithm (GA). The used GA comprises real-value coding with specific behavior taking each code as it is (whether binary, integer, or real), rank selection, and efficient uniform genetic operators. The results indicated, in comparison with the other applied optimization methods (linear, dynamic, Monte Carlo and heuristic search methods), that the real GA produces significantly the lowest distance (least cost tour) solution. It is concluded that the real GA approach is robust and it represents an efficient search method and is easily applied to nonlinear and complex problems of the TSP in the field of solid waste routing system in the large cities.
1 citations
01 Dec 2010
TL;DR: Complex number encoded mode was introduced to GA in order to expand the search region and avoid getting into the local minimum and results showed that it was useful for function optimization.
Abstract: The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete functions problems However, a simple GA may suffer from premature and can't find the global minimum value In this study complex number encoded mode was introduced to GA in order to expand the search region and avoid getting into the local minimum Experiment results showed that it was useful for function optimization
1 citations
01 Jan 2011
1 citations