Evolutionary Operators in Global Optimization with Dynamic Search Trajectories

One of the most commonly encountered approaches for the solution of unconstrained global optimization problems is the application of multi-start algorithms. These algorithms usually combine already computed minimizers and previously selected initial points, to generate new starting points, at which, local search methods are applied to detect new minimizers. Multi-start algorithms are usually terminated once a stochastic criterion is satisfied. In this paper, the operators of the Differential Evolution algorithm are employed to generate the starting points of a global optimization method with dynamic search trajectories. Results for various well-known and widely used test functions are reported, supporting the claim that the proposed approach improves drastically the performance of the algorithm, in terms of the total number of function evaluations required to reach a global minimizer.

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