中文摘要：

为了解决复杂的水资源优化配置问题和丰富智能优化方法在水资源优化配置中的应用，建立了以经济、社会、环境综合效益最大为目标的水资源优化配置模型和多目标鱼群-蚁群算法。经济效益以区域供水带来的直接经济效益最大为目标；社会效益以区域总缺水量最小为目标；生态环境效益以区域重要污染物排放量最小为目标；约束条件包括供水、需水、水环境和经济发展协调度等。多目标鱼群-蚁群算法融合了人工鱼群算法的快速跟踪变化和跳出局部极值优点以及蚁群算法的信息素正反馈优点，并将人工鱼群算法中的拥挤度概念引入到蚁群算法中，避免了蚁群算法初期可能早熟的问题。通过实验仿真，此算法具有较快的收敛速度和较高的寻优性能，能有效地找到优化解，从而为解决复杂的水资源优化配置问题提供了新的思路。

英文摘要：

To resolve complex problems on optimal allocation of water resources with intelligent optimal methods, a multi-objective optimization model was built and the multi-objective fish-ant colony algorithm （MFACA） was designed. This model, based on principles of efficiency, fairness, and harmoniousness, is aimed at producing the largest economic, social, and environmental benefits. The objective of economic benefit is the largest direct economic benefit produced by regional water supply. The objective of social benefit is referred to as the smallest regional water deficit. The objective of environmental benefit is to ensure the smallest discharge of major contaminants. Constraints included water supply, water demand, water settings, economic development, and its harmony. In this model, constraints of water supply include possible water yield and ground water yield. Constraints of water demand include living, industrial, agricultural, and environmental water. Constraints of water settings include overall merit index and water quality. The optimal allocation model had the characteristics of large-scale system, multiple objectives, multiple constraints, multiple levels, and multiple associations. To solve this complicated model, the multi-objective fish-ant colony algorithm was established in accordance with the integration of pheromone positive feedback of the ant colony optimization （ACO） and fast track change and jumping out of local extremum of the artificial fish-swarm algorithm （AFA）. A swarm degree in the AFA was used to avoid possible premature problems at the initial stage of ACO. It was not strict for MFACA to set parameters and initial values of a mathematical model. The objective functions and constraints were not necessarily continuous and differentiable. This algorithm has a faster convergence rate and a higher optimization power. In order to validate the feasibility and effectiveness of the MFACA, surveys were done in Zhenping County, Henan Province, China. Data of water resources and other rele