Maximizing profit in financial time series, like foreign exchange, with computational intelligence techniques is very challenging. It is even more challenging to make a decision from a multi-objective problem, like automated foreign exchange (Forex) trading. This study explores the effects of five decision models on three state-of-the-art dynamic multi-objective optimization algorithms namely, dynamic vector-evaluated particle swarm optimization (DVEPSO), multi-objective particle swarm optimization with crowded distance (MOPSO-CD) and dynamic non-dominated sorting genetic algorithm (DNSGA-II). A sliding window mechanism is employed over the USDZAR currency pair. The results show that each decision model generates different net profit. However, gray relational analysis (GRA) and objective sum (SUM) consistently performed better across all algorithms and technical indicators (relative strength index (RSI) and moving average convergence divergence (MACD)) than other decision models. Moreover, DNSGA-II was the most stable algorithm.