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Particle swarm optimization (PSO), differential evolu- tion (DE) and multi-parents crossover (MPC) are the evo- lutionary computation paradigms, all of which have shown superior performance on complex non-linear function op- timization problems. This paper detects the underlying re- lationship between them and then qualitatively proves that these heuristic approaches from different theoretical prin- ciples are consistent in form. Comparison experiments in- volving eight test functions well studied in the evolutionary optimization literature are used to highlight some perfor- mance differences between the techniques. The results from our study show that DE generally outperforms the other al- gorithms.