A global optimization based on physicomimetics framework
Liping Xie, Jianchao Zeng · 2009
Based on physicomimetics framework, this paper presents a global optimization algorithm inspired by physics, which is a stochastic population-based algorithm. In the approach, each physical individual has a position and velocity which move through the feasible region of global optimization problem under the influence of gravity. The virtual mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. An attraction-repulsion rule is constructed among individuals and utilized to move individuals towards the optimality. Experimental simulations show that the algorithm is effective.