Open AccessHybridization of Genetic Algorithm and Artificial Immune System for Assignment Problem
Author(s) -
La Ode Muhammad Farhan,
Zainudin Zukhri
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/803/1/012024
Subject(s) - genetic algorithm , premature convergence , convergence (economics) , computer science , local optimum , mathematical optimization , reliability (semiconductor) , selection (genetic algorithm) , algorithm , population , clonal selection , artificial intelligence , mathematics , power (physics) , physics , demography , quantum mechanics , sociology , immunology , economics , biology , economic growth
Genetic Algorithms (GA) have proven its reliability to obtain near optimum for complex problems. However, to optimize solutions and prevent premature convergence, operators on the GA were highly dependent on large population sizes, causing the computational speed to be slower than newer algorithms. Therefore, in this study, we propose a way to cause a significant improvement in GA for a higher quality of solutions and lower computing costs yet easy to implement. Hence, we analyzed the clonal selection algorithm from AIS to inspire genetic processes in GA. We then compared the performance of the proposed algorithm called HGA as with the GA to solve the assignment problem. Simulation results showed that HGA performed better in case of preventing on being stuck with the local optima as it accurately obtains an average of the optimum solution up to 100% for every given dataset while also reducing computational costs as it has less generation.