Pollution source parameter identification (PSPI) is significant for pollution control, since it can provide important information and save a lot of time for subsequent pollution elimination works. For solving the PSPI problem, a large number of pollution sensor nodes can be rapidly deployed to cover a large area and form a wireless sensor network (WSN). Based on the measurements of WSN, least-squares estimation methods can solve the PSPI problem by searching for the solution that minimize the sum of squared measurement noises. They are independent of the measurement noise distribution, i.e., robust to the noise distribution. To search for the least-squares solution, population-based parallel search techniques usually can overcome the premature convergence problem, which can stagnate the single-point search algorithm. In this paper, we adapt the relatively newly presented artificial bee colony (ABC) algorithm to solve the WSN-based PSPI problem and verifies its feasibility and robustness. Extensive simulation results show that the ABC and the particle swarm optimization (PSO) algorithm obtained similar identification results in the same simulation scenario. Moreover, the ABC and the PSO achieved much better performance than a traditionally used single-point search algorithm, i.e., the trust-region reflective algorithm.