TY - JOUR
T1 - Grasshopper optimization algorithm for multi-objective optimization problems
AU - Mirjalili, Seyedeh Zahra
AU - Mirjalili, Seyedali
AU - Saremi, Shahrzad
AU - Faris, Hossam
AU - Aljarah, Ibrahim
PY - 2018/4/1
Y1 - 2018/4/1
N2 - This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.
AB - This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85026797537&partnerID=8YFLogxK
U2 - 10.1007/s10489-017-1019-8
DO - 10.1007/s10489-017-1019-8
M3 - Article
AN - SCOPUS:85026797537
SN - 0924-669X
VL - 48
SP - 805
EP - 820
JO - Applied Intelligence
JF - Applied Intelligence
IS - 4
ER -