The surprisingly popular decision in social science fields is a wisdom of the crowd technique that taps into the expert minority opinion within a crowd, which has been demonstrated to be remarkably effective for multiple questions. Most of the existing PSO variants construct the exemplars by solely using fitness, which could be viewed as the democratic approaches or methods. However, the democratic methods tend to highlight the most popular opinion, not necessarily the most correct, which might lead the population into a local trapping region in the scenarios of swarm intelligent computing and evolutionary computation. This paper proposes a method to implement the surprisingly popular decision in PSO to facilitate the exemplar construction, cooperating with the dynamic topology maintenance. The proposed PSO variant is called the Surprisingly Popular Algorithm-based Comprehensive Adaptive Topology Learning Particle Swarm Optimization (SPA-CatlePSO). By using the dynamic topological connection and surprisingly popular decision strategy, the proposed SPA-CatlePSO could adjust the degree of small world topology, mimicking the mechanism of knowledge conversion in the crowd, and guide the direction of the exploitation by constructing exemplars with the largest surprisingly popular degree. We evaluate the proposed SPA-CatlePSO on the full CEC2014 benchmark suite and compare its validity with OLPSO, TSLPSO, ASDPSO, HCLPSO, OptBees and L-shade. The experimental results show that the SPA-CatlePSO algorithm is competitive with the most advanced swarm-based intelligent algorithms.