The Application Of Chaos Particle Swarm Optimization Algorithm To Water Pollution Control System

Water pollution control system planning was proposed after60years of the20thcentury that is a new interdisciplinary subject. It is a multi-variable, multi-objectiveand multi-level complex system involving almost all of the disciplines. It should becombined with the factors such as political, economic, technical level at the time andso on, and use the views such as global and local, development and links view to dealwith the problems of the water pollution control. Water pollution control systemplanning is an iterative process of decision-making, which is based on national laws,regulations and standards as the fundamental basis, the current technology andregional economic development plans as the guide, the best regional comprehensivewater pollution control system efficiency as the goal, with minimal economic costsfor the better water quality, so as to achieve the best social benefits. Therefore, thestudy of the water pollution control system is significance for the sustainabledevelopment of our country’s economy, environment and social. However, due to thecomplexity of water pollution control system, the traditional solution method can notget satisfactory results. Based on the chaotic particle swarm optimization algorithmhas features like mathematical conditions for relaxed, fast convergence, simpleprogramming and convenient, this paper tries to research the water pollution controlsystem with chaotic particle swarm optimization algorithm. As for water pollutioncontrol system consists of many elements, therefore, here only uses two examplesabout the optimal discharge of water pollution control system and the multi-objectiveoptimization problems to verify the feasibility of chaotic particle swarm algorithmapplies to this field. It is needed to establish the objective function and fitnessfunction when planning the water control system. Since the fitness function has greatimpact to the optimization results, its’ selection is key step of the planning process.Penalty function is used as fitness function commonly, but it has shortcomings that itsown can not overcome, therefore, constraint fitness priority-based ranking method isselected as fitness function. The optimization results that obtained by using thechaotic particle swarm compared with the results of standard particle swarmoptimization, genetic algorithms and MATLAB optimization function in the exampleapplications. The results show that chaotic particle swarm optimization algorithm hasfast convergence, superior search performance and its’ solution is proved better than the latter two. So the feasibility and effectiveness of chaotic particle swarmoptimization algorithm is also verified.