本研究應用一個新的啟發式演算法—『模擬電磁演算法』於類神經網路訓練，並利用此類神經網路來分析零售商營運模式中模擬系統的主要輸入參數與輸出成效的關係。模擬電磁神經網路應用電磁理論中電荷同性相斥與異性相吸的原理，來修正神經網路連結權重值，使其收斂至最佳權重值，而不會落入區域最佳解。研究結果顯示，模擬電磁演算法收斂的速度比遺傳演算法及倒傳遞演算法更為快速且更省記憶體空間。

　　　　　This paper applies a heuristic algorithm, called the “Electromagnetism Algorithm” (EM) [3], for neural network training. We develop a meta-model of the relationships between key inputs and performance measures of an apparel retail operations using neural network technology. This method simulates the electromagnetism theory of physics by considering each weight connection in a neural network as an electrical charge. Through the attraction and repulsion of the charges, weights move toward the optimality without being trapped into local optima like other algorithms such as genetic algorithm and gradient descent method. The computation results show that the EM algorithm not only converges much faster than those of genetic algorithms and back propagation algorithms in terms of CPU time but also saves more memories than those in genetic algorithms and back propagation algorithms.