A Modified Shuffled-frog-leaping Algorithm for Optimizing Bridge-deck Repairs

Infrastructure assessment and renewal have been in the center of attention worldwide. While the infrastructure is the foundation for economic growth, a large percentage of existing facilities are rapidly deteriorating due to age, outdated technology, and insufficient capacity for population growth. Prioritizing infrastructure facilities for repair purposes, the allocation of the limited funds, and the selection of appropriate repair methods, however, is a complex task, particularly when hundreds of facilities are involved. In this case, the complexity and huge problem size render traditional mathematical optimization tools as inadequate. Recently, a new breed of evolutionary algorithms has evolved with non-traditional optimization tools with great potential for application in the infrastructure management domain. Evolutionary algorithms, such as shuffled frog leaping, are stochastic search methods that mimic natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at optimum or near-optimum solutions to complex and large-scale optimization problems which cannot be solved by traditional mathematical optimization techniques. As a new and promising technique, the shuffled frog leaping algorithm draws its formulation from two other search techniques: the local search of the “particle swarm optimization” technique; and the competitiveness mixing of information of the “shuffled complex evolution” technique. In this paper, a modified shuffled frog leaping algorithms is presented and applied on a network of bridges to optimize the repair decisions for the bridge decks. The objective function is formulated to minimize the life-cycle cost of bridge deck repairs. The system’s implementation on a spreadsheet program is briefly highlighted. Also, the results of using the modified shuffled frog leaping are compared with that of the genetic algorithms.