A hybrid sperm swarm optimization and genetic algorithm for unimodal and multimodal optimization problems
Document Type
Article
Publication Date
1-1-2022
Abstract
A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.
Keywords
Genetic algorithms, Metaheuristics, Particle swarm optimization, Convergence, Standards, Simulated annealing, Genetics, Algorithm design and analysis, Optimization, Algorithm, Genetic algorithm, Hybrid, Metaheuristic, multimodal, Optimization, Sperm swarm optimization, Unimodal
Divisions
fsktm
Funders
Ministry of Higher Education Malaysia Fundamental Research Grant Scheme (FRGS) [FRGS/1/2019/ICT03/UM/02/2]
Publication Title
IEEE Access
Volume
10
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA