  The scheduling of multi-user remote laboratories is modeled as a multimodal
function for the proposed optimization algorithm. The hybrid optimization
algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated
Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem
for the remote laboratories to coordinate shared access. The proposed algorithm
utilizes the Simplex algorithm in terms of exploration, and NSGA for sorting
local optimum points with consideration of potential areas. The proposed
algorithm is applied to difficult nonlinear continuous multimodal functions,
and its performance is compared with hybrid Simplex Particle Swarm
Optimization, Simplex Genetic Algorithm, and other heuristic algorithms.
