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

Electronics

Muzammil Jusoh¹  SitiJulia Rosli¹  MohdNajib Mohd Yasin¹  KhairulNajmy Abdul Rani¹  Ruzelita Ngadiran¹  HaslizaA Rahim¹  AllanMelvin Andrew¹  Thennarasan Sabapathy¹  R.Badlishah Ahmad²  Mohamedfareq Abdulmalek³  NorZakiah Yahaya⁴ 

[1] Advanced Communication Engineering, Centre of Excellence (CoE), Universiti Malaysia Perlis (UniMAP), 01000 Kangar, Perlis, Malaysia;Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia;Faculty of Engineering and Information Sciences, University of Wollongong in Dubai, Dubai 20183, UAE;Physics Section, School of Distance Education, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia;

关键词: computational intelligence;    sine cosine;    cuckoo search;    metaheuristic;    optimization algorithm;    hybrid algorithm;   

DOI  :  10.3390/electronics9111786

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown