An enhanced multiobjective bacterial foraging algorithm for the compaction parameter optimization of earth-rock dams

Abstract

Compaction parameters can act as key decision variables. However, there is a lack of research on the optimization of compaction parameters considering quality, time, and costs. Existing algorithms have difficulty coping with this highly complex and nonlinear problem. To address this issue, an enhanced multiobjective bacterial foraging algorithm is proposed for searching the optimal compaction parameters of earth-rock dams. The cost and time are taken as multiple objectives, and the compaction quality is considered the main constraint because it must meet a certain standard. Then, the enhanced multiobjective bacteria foraging algorithm (EMOBFA) is proposed, which integrates multiple intelligent components into the fundamental single-objective bacterial foraging algorithm, including chaotic mapping, an adaptive step, a rotation learning strategy, and quantum computing. This further improves the speed and accuracy of the state-of-the-art multiobjective optimization algorithm. Finally, multiobjective test functions are used to compare the EMOBFA with commonly used multiobjective optimization algorithms to verify its generality, power, and scalability. A real-life large-scale water conservancy project in Southwest China shows that the proposed EMOBFA has realized the optimization of compaction parameters in the prior control of rolling construction.