TY - JOUR
T1 - MOAVOA
T2 - a new multi-objective artificial vultures optimization algorithm
AU - Khodadadi, Nima
AU - Soleimanian Gharehchopogh, Farhad
AU - Mirjalili, Seyedali
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems.
AB - This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems.
KW - Algorithm
KW - Artificial vultures optimization algorithm
KW - Multi-objective problem
KW - Optimization
KW - Pareto optimal solution
KW - Particle Swarm Optimization
KW - Performance indicator
UR - http://www.scopus.com/inward/record.url?scp=85136237789&partnerID=8YFLogxK
U2 - 10.1007/s00521-022-07557-y
DO - 10.1007/s00521-022-07557-y
M3 - Article
AN - SCOPUS:85136237789
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
ER -