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The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). This program generates a Latin Hypercube Sample by creating random permutations of the first n integers in each of k columns and then transforming those integers into n sections of a standard uniform distribution. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. Latin Hypercube sampling generates more efficient estimates of desired parameters than simple Monte Carlo sampling. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution.
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First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. This method extends the goal of univariate uniformity used by LHS to a multivariate situation. We propose a modified LHS method, LHSMDU, that enforces multidimensional uniformity. The Video will include: Description of Latin hypercube sampling.
Modified latin hypercube sampling how to#
In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. Latin Hypercube Sampling with Multidimensional Uniformity. In this video, you will learn how to carry out random Latin hypercube sampling in R studio. The metaheuristic algorithm is a popular research area for solving various optimization problems.