Abstract
Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called “variable mesh optimization” (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive algorithm.
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Appendix: Results of the experiments
Appendix: Results of the experiments
In this appendix, the results used for the statistical analysis for each study case are presented. Each algorithm is run 25 times for each test function, and the average error of the best found solution is computed. The function error value for a solution x is defined as (f(x j ) − f(x * j )), where x * j is the global optimum of the function. The captions in the tables detail which experiment it belongs to and the dimension of the test functions used (see Tables 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25).
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Puris, A., Bello, R., Molina, D. et al. Variable mesh optimization for continuous optimization problems. Soft Comput 16, 511–525 (2012). https://doi.org/10.1007/s00500-011-0753-9
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DOI: https://doi.org/10.1007/s00500-011-0753-9