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Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
book

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
October 2017
Intermediate to advanced content levelIntermediate to advanced
304 pages
8h 3m
English
Wiley
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

11 Shuffled Frog‐Leaping Algorithm

Summary

This chapter describes the shuffled frog‐leaping algorithm (SFLA), which is a swarm intelligence algorithm based on the memetic evolution of the social behavior of frogs.

11.1 Introduction

The shuffled frog‐leaping algorithm (SFLA) is a swarm intelligence algorithm based on the social behavior of frogs. It was proposed by Eusuff and Lansey (2003). Eusuff et al. (2006) demonstrated the capability of the SFLA for calibrating groundwater models and to design water distribution networks problems. They also compared the results of the SFLA with those of the genetic algorithm (GA). The comparison proved that the SFLA can be an effective tool for solving combinatorial optimization problems. Chung and Lansey (2008) developed a general large‐scale water supply model to minimize the total system cost by integrating a mathematical supply system representation applying the SFLA. The results showed that the SFLA found solutions that satisfied all the constraints for the studied networks. Seifollahi‐Aghmiuni et al. (2011) implemented the SFLA to analyze the efficiency of a designed network based on nodal demand uncertainty during the operational period. Zhao et al. (2011) presented a combined water quality assessment model constructed based on artificial neural network (ANN) and the SFLA, which was applied to train the initialized data from water quality criteria. Balamurugan (2012) applied the SFLA to achieve the optimum solution of economic ...

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Publisher Resources

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