An Improved Multi-Objective Particle Swarm Optimization With TOPSIS and Fuzzy Logic for Optimizing Trapezoidal Labyrinth Weir

Labyrinth Weir (LW) is a popular control structure that passes significantly higher flow rate compared to the linear weirs. In order approach optimal design of trapezoidal LW, multi-objective problem defined concurrently minimize LW consumed concrete volume and maximize its discharge capacity. Simultaneously, Radial Basis function Neural Networks (RBFNN) designed used for estimating coefficient (Cd) according existing experimental results. An improved particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) proposed solve problem. This utilizes Technique Order Preference by Similarity Ideal Solution (TOPSIS) rank solutions, while fuzzy inference system developed select strategy finding two leaders among non-dominated solutions. The performance TFMOPSO has been tested on Ute dam. results TFMOPSO, along with three other state-of-the-art algorithms, are explored in terms hypervolume, coverage, spacing metrics. It demonstrated outperforms algorithms studies solving case Also, RBFNN found be one most appropriate approaches studied Pareto solutions from exhibit significant improvement original dam LW.