MOAVOA: a new multi-objective artificial vultures optimization algorithm

Nima Khodadadi, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


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.

Original languageEnglish
JournalNeural Computing and Applications
Publication statusPublished - 2022


  • Algorithm
  • Artificial vultures optimization algorithm
  • Multi-objective problem
  • Optimization
  • Pareto optimal solution
  • Particle Swarm Optimization
  • Performance indicator


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