Multi-agent hybrid particle swarm optimization (MAHPSO) for wastewater treatment network planning

The planning of municipal wastewater treatment networks has been recognized as a valuable means for achieving the optimal use of resources and improving the cost-efficient operation of plants. In this study, a multi-agent hybrid particle swarm optimization (MAHPSO) approach was developed as a new systematic planning tool by the integration of hybrid particle swarm optimization and multi-agent system. Its effectiveness and feasibility were demonstrated by a simplified real-world case in the metropolitan area of St. John's, Canada. Genetic algorithm and hybrid particle swarm optimization were also employed for comparison and the results indicated the better performance with the proposed approach in terms of efficiency in finding the solutions, computational requirements, and overall costs of the network. The MAHPSO can be used as an effective evolutionary algorithm in supporting wastewater treatment network operation and other complex environmental management problems.