A novel modified flower pollination algorithm for global optimization

The flower pollination algorithm (FPA) is a relatively new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. Indeed, this algorithm is based globally on two main rules: the global pollination (biotic and cross-pollination) and the local pollination (abiotic and self-pollination). The random permutation between these latter allows to keep a permanent balance between intensification and diversification. However, this procedure causes an involuntary orientation toward a bad solution (local optima). In addition, FPA illustrates an inadequacy in terms of intensification and diversification of new solutions; this has become clear when the complexity of the treated problem is increased. Further, FPA has also another insufficiency, which is its slow convergence rate caused in principle by its weak intensification. In this paper, to overcome these weaknesses, we have introduced some modifications on the basic FPA algorithmic structure based on the two following improvements: (1) Generating a set of global orientations (toward global or local pollination) for all members of the population. Indeed, each element (global orientation) in this set is composed of a fixed number (equal to the population size) of sub-random orientation. Thus, the number of elements is fixed by the designer, which enhances significantly the diversification characteristic. (2) Constructing a set of best solution vectors relating to all generated global orientations. In fact, this set is compared at each iteration to a fixed number of actual solution vectors to select the best among them based on their fitness values. The proposed algorithm called novel modified FPA (NMFPA) with its novel algorithmic structure offers to researchers the opportunity to: (1) use it in their comparison study (e.g., with others FPA proposed variants) and (2) develop other new methods or techniques based on its novel integrated mechanisms. To demonstrate the performance of this new FPA variant, a set of 28 benchmark functions defined in IEEE-CEC’13 and a 15 real-world numerical optimization problems proposed in the IEEE-CEC’11 are employed. Compared with FPA, two its famous variants and other state-of-the-art evolutionary algorithms, NMFPA shows overall better performance.