Particle Swarm Optimization (PSO) algorithms have attracted significant attention in continuous optimization due to their simple structure and strong global search capabilities. However, PSO is prone to premature convergence when dealing with complex multimodal functions and lacks an effective mechanism for maintaining diversity, which degrades search efficiency and stability. To address this problem, this paper proposes a dual-population heterogeneous adaptive cooperative particle swarm optimization algorithm (DHACPSO). First, the algorithm constructs a dual-population heterogeneous structure, where one group of particles updates positions based on local topology to enhance local exploitation capability, and incorporates a difference-aware cooperation mechanism to enable adaptive utilization of information from the other subpopulation. Meanwhile, the other group of particles incorporates Lévy flight to enhance the ability to escape from local optima. Moreover, an entropy-aware control strategy is introduced throughout the population to maintain search diversity. Finally, extensive experiments are conducted on the CEC2017 benchmark suite, as well as on the three-dimensional wireless sensor networks (3D WSNs) coverage optimization problem. The experimental results demonstrate that DHACPSO exhibits significant competitive advantages over the state-of-the-art PSO variants.