Tabu search for nonlinear and parametric optimization (with links to genetic algorithms)
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem
Journal of Global Optimization
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Scatter search for chemical and bio-process optimization
Journal of Global Optimization
An evolutionary method for complex-process optimization
Computers and Operations Research
Hybrid heuristics for the maximum diversity problem
Computational Optimization and Applications
C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Annealing evolutionary stochastic approximation Monte Carlo for global optimization
Statistics and Computing
Computers and Operations Research
Computers and Operations Research
A scatter search algorithm for the automatic clustering problem
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Evolutionary elementary cooperative strategy for global optimization
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Application of data mining in a global optimization algorithm
Advances in Engineering Software
A black-box scatter search for optimization problems with integer variables
Journal of Global Optimization
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Scatter search is an evolutionary method that, unlike genetic algorithms, operateson a small set of solutions and makes only limited use of randomization as a proxy fordiversification when searching for a globally optimal solution. The scatter search framework is flexible, allowing the development of alternative implementations with varying degrees ofsophistication. In this paper, we test the merit of several scatter search designs in the context of global optimization of multimodal functions. We compare these designs among themselves and choose one to compare against a well-known genetic algorithm that has been specifically developed for this class of problems. The testing is performed on a set of benchmark multimodal functions with known global minima.