Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm

• Published in: Environmental science and pollution research international Vol. 30; no. 60; pp. 126116 - 126131
• Main Authors: Haghdoost, Shekoofeh, Niksokhan, Mohammad Hossein, Zamani, Mohammad G., Nikoo, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Cuculidae; Discharge; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental protection; Fresh Water; Genetic algorithms; Immunological tolerance; Iran; Multiple objective analysis; Optimization algorithms; Optimization models; Performance indices; Pollution tolerance; Population density; Population number; population size; Quality standards; Research Article; River systems; river water; Rivers; Simulation models; Sorting algorithms; Waste load; Waste Water Technology; Wasteload allocation; Water Management; Water Pollution; Water Pollution Control; Water Quality; Water quality standards; Iran; Waste load allocation (WLA); Pareto front; Multi-objective optimization (MOO); Non-dominated sorting genetic algorithm (NSGA-II); Cuckoo optimization algorithm (COA)
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-31058-7

Water pollution escalates with rising waste discharge in river systems, as the rivers’ limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA’s Pareto front is superior to NSGA-II, which highlights the MOCOA’s effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.