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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.

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