Application of Multi-objective Particle Swarm Optimization Algorithm in Integrated Marketing Method Selection

• Authors: Qiwan Wang
• Venue: ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
• Year: 2009

Through multi-particle swarm optimization algorithm, this paper is aimed to solving the optimization problems of multi-production and multi-marketing strategy selection during the process of integrated marketing. In order to achieve benefit maximization, the fittest marketing method should be put in place into the marketing promotion of each product, which in fact is the problem of multi-objective optimization decision. During the optimization process, first of all, convert discrete variable into continuous variable through the equivalent probability matrix, then update particle swarm and normalize particle position, and finally complete the selection of particle individual extremum and the global extremum through decoding and fitness computing. The simulation results for the practical problem through this method show that the investment and rationalized distribution of marketing methods can obtain better expected benefits. The conclusion is that multi-objective particle swarm optimization algorithm is an effective method for solving the optimization allocation of products and marketing methods during the process of integrated marketing.