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Multi-modal function optimization with the ZEDS (zoomed evolutionary dual strategy algorithm). (English) Zbl 1062.65068
The authors present a new evolutionary algorithm for multi-modal function optimization called zoomed evolutionary dual strategy (ZEDS). ZEDS employs a two-step, zoomed (global to local), evolutionary approach. In the first(global) step, an improved ’GT algorithm’ is employed to perform a global recombinatory search that divides the search space into niches according to the positions of its approximate solutions. In the second (local) step, ’a niche evolutionary strategy’ performs a local search in the niches obtained from the first step, which is repeated until acceptable solutions are found.
The ZEDS algorithm is applied to several multi-modal optimization problems. The algorithm has performed better ( or at least equal to other algorithms) on the challenging problems and it is relatively easy to implement. The convergence of the algorithm is proved, too.

65K05 Numerical mathematical programming methods
90C29 Multi-objective and goal programming
90C30 Nonlinear programming
Full Text: DOI
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