The Improvement And Application Of Flower Pollination Algorithm In Multi-objective Optimization

The multi-objective pollination algorithm(MOFPA)is a new heuristic algorithm.The algorithm has been applied to engineering optimization problems because it can be conbergenced quickly.The main contents of this paper are as follows:(1)This paper describes the biological mechanism,the basic steps and its research status of the pollination algorithm,and analyzes its advantages and disadvantages.(2)In order to improve the efficiency in position during the process of iteration with flower pollination algorithm in multi-objective optimization(MOFPA),the idea of simulated annealing and Gaussian perturbations are used.This paper proposed an improved flower pollination algorithm in multi-objective optimization based on simulated annealing Gaussian disturbance(SGMOFPA),then compare the algorithm with MOFPA and MOPSO in four classical test functions.The algorithm experiments show that the proposed strategies can improve the convergence speed and the spacing of solutions are effectively.(3)In view of solving the portfolio optimization problem for a power generation company(Gen Co),using the MOFPA to construct the Pareto optimal solution set and apply it to the investment portfolio optimization problem in the electricity market.To avoid under-diversification,an additional objective to enhance the diversification benefit is proposed alongside with the four original objectives of the MVS-D portfolio framework,and compare the simulation with MOPSO and MOGAS.The results show that MOFPA have made possible the inclusion of the optimization framework that produces Pareto fronts that also cover those based on the traditional MVS-D framework,thereby offering better trade-off solutions while promoting investment diversification benefits for power generation companies.