Open AccessLeaders and followers algorithm for constrained non-linear optimization
Author(s) -
Helen Yuliana Angmalisang,
Syaiful Anam,
Sobri Abusini
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v13.i1.pp162-169
Subject(s) - metaheuristic , mathematical optimization , benchmark (surveying) , differential evolution , optimization problem , particle swarm optimization , continuous optimization , constrained optimization , constraint (computer aided design) , algorithm , meta optimization , mathematics , multi swarm optimization , computer science , geometry , geodesy , geography
Leaders and Followers algorithm was a novel metaheuristics proposed by Yasser Gonzalez-Fernandez and Stephen Chen. In solving unconstrained optimization, it performed better exploration than other well-known metaheuristics, e.g. Genetic Algorithm, Particle Swarm Optimization and Differential Evolution. Therefore, it performed well in multi-modal problems. In this paper, Leaders and Followers was modified for constrained non-linear optimization. Several well-known benchmark problems for constrained optimization were used to evaluate the proposed algorithm. The result of the evaluation showed that the proposed algorithm consistently and successfully found the optimal solution of low dimensional constrained optimization problems and high dimensional optimization with high number of linear inequality constraint only. Moreover, the proposed algorithm had difficulty in solving high dimensional optimization problem with non-linear constraints and any problem which has more than one equality constraint. In the comparison with other metaheuristics, Leaders and Followers had better performance in overall benchmark problems.