A Hybrid Modified Method of the Sine Cosine Algorithm Using Latin Hypercube Sampling with the Cuckoo Search Algorithm for Optimization Problems

Abstract

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. 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. 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. 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. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. 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). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

“…The new sample size generated by the Latin Hypercube Sampling (LHS) method exhibits higher stability and wider implementation in the SCA adjustment. The details of SCA algorithm and LHS method expressions in [27].…”

“…SCA cannot guarantee that the optimal solution is achieved in one operation. However, when the initial value setting and the number of iterations set are sufficiently large, the optimal solution's searching is enhanced significantly [10,27]. Other recent evolutionary computation algorithms such as MBO, EWA, EHO, and MSA [28] have been widely applied in engineering problems where these algorithms exhibit good performances in real engineering application [29].…”

Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

“…In this section, the proposed ASCA algorithm is evaluated in compared with PSO [42], [43], WOA [44], [45], GA [46],GWO [47], SSA [48], HHO [32], [49], HGSCADE [50], HMSCACSA [51], MPA [52], ChOA [53], and SMA [54] algorithms. For a fair comparison, the proposed ASCA algorithm and the compared algorithms begin in the experiment with the same number of agents (population) with same size and are applied to the same objective function using same number of iteration, dimensions, and boundaries.…”

“…A dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon) is used for the experiments. The proposed ASCA algorithm is evaluated in compared with Particle Swarm Optimizer (PSO) [42], [43], Whale Optimization Algorithm (WOA) [44], [45], Genetic Algorithm (GA) [46], Grey Wolf Optimizer (GWO) [47], Squirrel search algorithm (SSA) [48], Harris Hawks Optimization (HHO) [32], [49], Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE) [50], Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) [51], Marine Predators Algorithm (MPA) [52], Chimp Optimization Algorithm (ChOA) [53], and Slime Mould Algorithm (SMA) [54]. Major contributions of our work are as follow:…”

Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

“…In addition to these methods, there are many improved LHS methods. However, this paper used the existing LHS method because initial sample generation is not a main idea [17][18][19][20][21].…”