Multiobjective evolutionary optimization of water distribution systems: exploiting diversity with infeasible solutions

Tiku T. Tanyimboh, Alemtsehay G. Seyoum

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
124 Downloads (Pure)


This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations of cost-effective feasible and infeasible solutions based on Pareto dominance. This yields a boundary search approach that also promotes diversity in the gene pool throughout the progress of the optimization by exploiting the full spectrum of non-dominated infeasible solutions. The relative effectiveness of small and moderate population sizes with respect to the number of decision variables is investigated also. The results reveal the optimization algorithm to be efficient, stable and robust. It found optimal and near-optimal solutions reliably and efficiently. The real-world system based optimisation problem involved multiple variable head supply nodes, 29 fire-fighting flows, extended period simulation and multiple demand categories including water loss. The least cost solutions found satisfied the flow and pressure requirements consistently. The cheapest feasible solutions achieved represent savings of 48.1% and 48.2%, for populations of 200 and 1000, respectively, and the population of 1000 achieved slightly better results overall.
Original languageEnglish
Pages (from-to)133-141
Number of pages9
JournalJournal of Environmental Management
Issue numberPart 1
Early online date30 Aug 2016
Publication statusPublished - 1 Dec 2016


  • water supply
  • dynamic simulation
  • constraint handling
  • infrastructure planning
  • minimum solution vector
  • maximum solution vector
  • computational efficiency
  • water distribution systems


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