Min-Der Lin

Scatter search heuristic for least-cost design of water distribution networks

The optimization problems of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. Heuristic algorithms such as genetic algorithms, simulated annealing, tabu search and ant colony optimization have been extensively employed over the last decade. This article proposed an optimization procedure based on the scatter search (SS) framework, which is also a heuristic algorithm, to obtain the least-cost designs of three well-known looped water distribution networks (two-loop, Hanoi and New York networks). The computational results obtained with the three benchmark instances indicate that SS is able to find solutions comparable to those provided by some of the most competitive algorithms published in the literature.

Application of immune algorithms on solving minimum-cost problem of water distribution network

Immune algorithm (IA) is a set of computational systems inspired by the defense process of the biological immune system. This study proposed an optimization procedure based on IA framework to optimize the designs of water distribution networks. A modified IA (mIA) procedure, which employs genetic algorithm (GA) to briefly screen initial antibody repertoires for IA, is also developed. The well-known benchmark instance, New York City Tunnel (NYCT) problem, is utilized as a case study to evaluate the optimization performance of IA and mIA. The least-cost designs of NYCT obtained by IA and mIA are compared with those by GA and fast messy GA previously published in the literature. The results of comparison reveal that IA and mIA are able to find the optimal solutions of NYCT with higher computational efficiency (less number of evaluations) than GA and fmGA. Notable performance enhancement is observed in mIA, indicating that the combination of GA can significantly improve the optimization performance of IA.

Applying tabu search and simulated annealing to the optimal design of sewer networks

Optimizations of sewer network designs create complicated and highly nonlinear problems wherein conventional optimization techniques often get easily bogged down in local optima and cannot successfully address such problems. In the past decades, heuristic algorithms possessing robust and efficient global search capabilities have helped to solve continuous and discrete optimization problems and have demonstrated considerable promise. This study applied tabu search (TS) and simulated annealing (SA) to the optimization of sewer network designs. For a case study, this article used the sewer network design of a central Taiwan township, which contains significantly varied elevations, and the optimal designs from TS and SA were compared with the original official design. The results show that, in contrast with the original designs failure to satisfy the minimum flow-velocity requirements, both TS and SA achieved least-cost solutions that also fulfilled all the constraints of the design criteria. According to the average performance of 200 trials, SA outperformed TS in both robustness and efficiency for solving sewer network optimization problems.

Optimal Scheduling of Booster Chlorination with Immune Algorithm

This paper describes the methodology and application of an immune algorithm (IA) scheme tailor-made for the EPANET for simultaneously optimizing the injection rates and scheduling of chlorine booster stations under the unsteady state of a water distribution network system (WDNS). The objective of this study is to initiate a total chlorination dose to satisfy the minimum and maximum required chlorine residual at every demand node in a WDNS while minimizing the chlorine consumption as much as possible. The decision variables are the scheduling of the mass injection rate at the booster node in the WDNS segments. The evaluation results confirm the potential of IA in solving the scheduling of booster disinfection optimization problems.

Application of simulated annealing on least-cost design of sewer network

This study employed simulated annealing (SA) to optimize minimum-cost design of sewer network. A sewer network design which contains significantly varied elevations was used as a case study. The results show that SA is able to achieve least-cost solutions which also fulfill all the constraints of design criteria. Based on the average performance of 200 trials, SA exhibits robustness and efficiency for solving sewer network system optimization problems.

Application of evolutionary computing on least-cost water distribution network designs

The optimization of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. This study adopts ant algorithm (AA), immune algorithm (IA), and scatter search (SS) which are all evolutionary computing techniques to avoid the entrapments by local optimal solutions, to obtain the leastcost designs of water distribution networks. One benchmark water distribution network optimization problems is used as a case study. Comparisons of the results of this study with relevant literature data indicate that AA and SS are able to find solutions better than those provided by some of the most competitive algorithms published in the literature. Furthermore, the results of the success rate evaluation indicate that AA can 100% successfully achieve the global optimum.

Application of heuristic algorithms on groundwater pumping source identification problems

This study investigates the application of heuristic algorithms on groundwater pumping source identification problem. Unknown groundwater extraction site is located by analyzing the hydraulic heads of the observation wells. Genetic algorithm (GA), particle swarm optimization (PSO), mutated PSO (MUPSO), their hybrid models GA-MUPSO, and groundwater simulation models are employed to solve the problems afore mentioned. The results indicate that all optimization models developed here can successfully locate the pumping source. Especially the GA-MUPSO hybrid model illustrated the best performance and efficiency, not only located the pumping source with a 100% success rate, but also required a far less CPU time than GA and PSO.