A novel three-phase trajectory informed search methodology for global optimization

A new deterministic method for solving a global optimization problem is proposed. The proposed method consists of three phases. The first phase is a typical local search to compute a local minimum. The second phase employs a discrete sup-local search to locate a so-called sup-local minimum taking the lowest objective value among the neighboring local minima. The third phase is an attractor-based global search to locate a new point of next descent with a lower objective value. The simulation results through well-known global optimization problems are shown to demonstrate the efficiency of the proposed method.

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